לוח משרות דרושים AI Engineer משרה מלאה, לחיפוש זה נמצאו 184 משרות

As a Senior Software Engineer specializing in the AI & ML ecosystem, you'll be a core contributor owning and evolving critical parts of AI ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Engineers and Data Scientists to harness speed and scale in the frameworks they already use.

We're looking for someone who has firsthand experience as an AI & MLEngineer or Data Scientist. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.

You'll own the full lifecycle of key AI/ML integrations, driving architecture, performance, and feature direction across AI & LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with as a vector store, ML feature stores, and LLM-powered data applications.

columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real: building the robust, production-ready connectors that make the natural choice when data practitioners design their next-generation AI and data systems.

What you'll do
Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design
Drive the AI/LLM integration strategy: designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications
Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback
Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities
Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows

Who We’re Looking For – The Team Lead & Architect Chargeflow is at the forefront of fintech and AI innovation. Our mission is to build a fraud-free global commerce ecosystem by leveraging cutting-edge technology—freeing online businesses to focus on their core ideas and growth. We’re looking for an experienced, hands-on AI Software Engineer Squad Lead to lead the team building the AI agents, intelligent workflows, and decision-making systems powering Chargeflow’s products. This role combines technical and architectural leadership with people leadership . You’ll lead engineers, build alongside them, and own production systems that must operate reliably, securely, and at scale.
This Is Not Your Typical Leadership Role If you’re looking to manage tasks, run standups, and stay above the code—this position probably isn’t for you. But if you want to build AI systems, shape engineers, and redefine how software teams operate in the age of AI—keep reading.
What We’re Actually Building At Chargeflow, we’re building systems that don’t just process data—they understand context, make decisions, and take action. Real-time. At scale. Under uncertainty. Our AI Platform brings together models, data, tools, business logic, and intelligent workflows to solve complex problems across the chargeback and fraud lifecycle. This isn’t a pure infrastructure or AI research role—and it isn’t about building impressive demos. It’s about turning rapidly evolving AI capabilities into reliable, production-grade products with measurable business impact.
What You’ll Actually Do
* Lead a team of strong software engineers—and build alongside them
* Own the architecture and evolution of Chargeflow’s AI Platform
* Build production-grade AI agents, intelligent workflows, and decision-making systems
* Combine LLMs with business logic, data, tools, and deterministic software
* Own AI systems across their lifecycle—from design and implementation to deployment, evaluation, and continuous improvement
* Make architectural trade-offs across accuracy, latency, scalability, reliability, security, and cost
* Turn ambiguous product and business problems into clear technical direction
* Raise the bar for code quality, system design, and engineering execution
* Help strong engineers become exceptional

We're looking for a Senior AI Solutions Builder to join our Product team. In this role, you will design, build, and ship AI-powered solutions that enhance productivity and drive measurable business value across the organization.

You will operate at the intersection of AI, data, pricing and business - owning the end-to-end development of agents, copilots, and workflow automations that directly inform decisions and eliminate manual work.

You are both a builder and an enabler. You ship solutions yourself and raise the AI capability floor for the teams around you. You'll collaborate closely with Product, Engineering, CS, Sales, and builders across the company to deliver outcomes that improve decision velocity, product and operational efficiency, and long-term growth.

You Will

Design and ship AI-powered copilots, agents, and decision-support tools for the Product team, starting with Pricing.

Build data pipelines and analytical models for pricing intelligence.

Develop autonomous agents that automate recurring Product tasks.

Automate manual workflows across product tools so the right action happens when a signal fires.

Build LLM-based scoring models that give roadmap and prioritization decisions a data backbone.

Enable and upskill product teams through AI playbooks, sessions, and hands-on co-building.

Collaborate with builders across Engineering, CS, GTM, and other teams to co-create solutions that scale.

Define and track impact: time-to-insight, decision velocity, manual hours eliminated, and business outcomes.

The company building an agentic development lifecycle, an infrastructure of autonomous agents that work alongside our engineers to accelerate and improve how we build software. Our goal is to ship faster, with higher quality, and to continuously tighten the feedback loop between what the agents produce and what engineering actually needs. Over time, this system should compound: every improvement makes the next one easier to reach.
we are an enterprise secure browser used by some of the largest organizations in the world. It's a complex, multidisciplinary product spanning browser core, frontend, extensions, and backend services, and it runs at scale for customers who need it to always work. The bar for what we ship is high. That means whatever agentic infrastructure we build has to meet the same standard. We're not here to vibe code our way to production.
We're looking for an AI Engineer with a product builder's mindset. You have real experience with AI and agentic workflows, and you know how to take a complex project from idea to adoption, technically and organizationally. That means working across teams, aligning with security, infrastructure, and other engineering groups, and understanding that building the system is only half the job. Getting people to trust it is the other half.
We aren't looking for a conventional senior developer; we need someone whose mindset is adapted to technical challenges that didn't even exist 18 months ago.

We develop the AI infrastructure that powers the AI experience throughout the product. We ensure the production AI platform is stable, reliable and secure.
Our mission is not only to build AI-powered features, but to enable every product team to build reliable, scalable, and high-quality AI experiences.
We work with modern LLM technologies, agentic workflows, MCP, retrieval systems, evaluation pipelines, distributed systems, and production-grade AI infrastructure, deploying to production multiple times a day.
Job responsibilities
What You'll Do
As a Tech Lead, you'll play a key role in shaping the future of AI .
You'll combine deep technical expertise with strategic thinking, helping multiple teams build production-ready AI experiences while driving engineering excellence across the organization.
In this role you will:
Lead the technical direction of one of AI domains.
Design scalable AI architectures and production-ready agentic systems.
Drive technical decisions around LLMs, Retrieval, Tool Calling, MCP, evaluation frameworks, and AI infrastructure.
Own complex cross-team initiatives from design through production.
Collaborate closely with Product, Data, Infrastructure, and Engineering teams to turn AI opportunities into customer value.
Help define engineering standards and best practices for building reliable AI systems.
Drive engineering excellence through architecture reviews, mentoring, and technical leadership.
Balance rapid experimentation with production-grade quality and scalability.
Evaluate emerging AI technologies and identify opportunities to improve our platform and customer experience.

As an AI Product Owner & AI Process and Agentic Designer, you will own the process of transforming complex workflows into scalable AI solutions, bridging the gap between business stakeholders and AI Engineers. You will lead business process analysis, identify agentic process opportunities, validate business value, and define solution designs that enable successful AI implementation across the organization.

Responsibilities

Business Process Analysis & Discovery
Partner with business stakeholders to map, document, and analyze existing workflows, identifying inefficiencies, bottlenecks, and manual processes ripe for automation.
Conduct discovery sessions and workshops to deeply understand business needs, pain points, and success criteria across departments.
Agentic Opportunity Identification
Evaluate workflows to pinpoint where agentic and AI-driven approaches can deliver the most value, distinguishing tasks suited to automation, augmentation, or full agentic orchestration.
Maintain a prioritized pipeline of AI/agentic opportunities, assessing each for feasibility, complexity, and impact.
Business Value Validation
Define and quantify the expected business value of proposed solutions (e.g., cost savings, time reduction, quality improvement, revenue impact) and build the business case for investment.
Establish success metrics and KPIs, and track realized value post-implementation to ensure outcomes match projections.
Solution Design & Definition
Translate business requirements into clear solution designs, including agentic process flows, decision logic, data inputs/outputs, integration points, and human-in-the-loop checkpoints.
Define guardrails, escalation paths, and fallback behaviors to ensure solutions operate safely and reliably.
Stakeholder & Engineering Bridging
Serve as the primary liaison between business stakeholders and AI engineers, translating business intent into technical requirements and engineering constraints back into business terms.
Own the product backlog, prioritize features, and clearly communicate scope, trade-offs, and timelines to all parties.
Implementation Enablement & Iteration
Guide solutions through the full lifecycle from concept to deployment, supporting testing, validation, and change management.
Gather feedback, monitor performance, and drive continuous refinement of deployed AI and agentic solutions.
Scalability & Standardization
Identify reusable patterns, components, and best practices to scale successful solutions across the organization.
Contribute to governance frameworks, documentation standards, and design principles for AI/agentic product development.

we are seeking an experienced and visionary ML Engineer to join our dynamic technology organization. The successful candidate will be a part of a team of talented AI Engineers, Building agentic AI solutions, driving innovation and delivering business value through advanced Generative AI solutions & machine learning techniques. This role requires a strategic thinker with hands-on expertise in both traditional and cutting-edge Gen AI and LLM methodologies and a passion for continuous learning and development.

Responsibilities

Build the solution: Own end-to-end technical delivery of agentic systems, from source-system integration through agent design, development, evaluation, and production deployment.
Integrate AI systems with source systems (Salesforce, Databricks, Splunk, internal APIs, business applications). Handle agent harness and orchestration.
Handle the operational handover to the business function and any necessary support transition.
Collaborate across teams: Partner closely with data engineering, platform, security, and business teams to align on requirements, dependencies, and integration points.
Engage stakeholders: Gather requirements directly from business functions, communicate technical trade-offs clearly, and keep stakeholders informed on progress, risks, and timelines.
Uphold quality and reliability: Establish and maintain best practices for code quality, testing, evaluation, monitoring, and observability of deployed AI systems.
Contribute to the team's technical growth through knowledge-sharing and help shape engineering standards and reusable patterns.
Stay current: Continuously evaluate emerging Gen AI, LLM, and agentic frameworks, and recommend tools and approaches that improve delivery speed and solution quality.

We are looking for a Principal Tech Lead to join our team.

So, what will you be doing all day?

As a Principal Tech Lead, your daily impact spans deep-tech execution, organizational mentorship, and bleeding-edge paradigm adoption:

Architectural Governance: Own the end-to-end design, scalability, and performance of complex backend distributed systems. Ensure architectures are resilient, modular, and optimized for intensive data workloads.
Agentic Engineering Innovation: Champion and embed Agentic SDLC paradigms, tools and solutions into the engineering lifecycle. Design frameworks that enable autonomous AI agents to build, test, and deploy features end-to-end, while enforcing rigorous quality gates and deterministic production guardrails.
Cross-Project Orchestration: Maintain an exceptionally large context window to actively guide and facilitate multiple highly technical streams and parallel projects simultaneously without losing focus or delivery speed.
Guild Leadership & Mentorship: Act as the primary technical sounding board for the engineering community. Lead backend guild sessions, introduce emerging technologies, and actively mentor senior and staff engineers to elevate the group's collective capabilities.
High-Velocity Execution: Lead by example. When taking on critical path, hands-on tasks, execute with unmatched speed and quality, demonstrating what peak operational efficiency looks like.

As an AI Engineer , you will be a key player in building our new AI platform, AI Studio. This platform aims to provide users with instant answers to complex market research questions, generate world-class reports, and create live, interactive, and powerful dashboards. Our goal is to reduce the complexity and time-to-insight for our users, helping them streamline their market tasks and achieve automation that drives real value.

So, what will you be doing all day?

Design and develop LLM-based intelligent agents that can handle complex market research queries.
Utilize LangGraph and other frameworks to create state of the art AI agents that integrate various tools and data sources.
Implement integrations that allow agents to take automated actions on behalf of users, streamlining workflows and reducing overhead.
Collaborate with product, data, and UX teams to ensure that AI solutions are aligned with user needs and business goals.
Continuously evaluate and improve the performance of AI models to ensure high-quality and data-driven results.

This is not your typical leadership role.
If youre looking to manage tasks, run standups, and stay above the code - this position probable isnt for you.
But if you want to build systems, shape engineers, and redefine how teams operate in the age of AI - keep reading.
What were actually building
were building systems that dont just process data - they make decisions.
Real-time. At scale. Under uncertainty.
Fraud evolves fast.
So our systems, and our engineers, need to evolve faster.
This is where you come in.
What youll actually do:
Youll lead a team - but youll also build with them
Youll make architectural decisions that directly impact how the system scales and behaves
Youll own critical parts of the system - not just people
Youll turn ambiguity into direction (for both product and engineering)
Youll raise the bar: in code quality, system design, and how the team thinks
Youll embed AI into how the team works - not as a tool, but as leverage
Youll help strong engineers become exceptional

Required AI Data Engineer
About the role:
We're building the intelligence layer that lets everyone ask hard questions of our data and get trustworthy answers - in plain language, in seconds. As AI Data Engineer, you own the semantic and AI-native surface of our Snowflake platform: the governed semantic layer that defines "what a metric means," the Cortex Agents that let business users query it conversationally, and the infrastructure that keeps all of it fast, reliable, and ready to scale.
This is a builder-owner role. You'll ship the semantic models, wire up the agents, and set the standard for how analysts across the company work with data. You'll also lead the Data Analyst guild - the connective tissue that keeps our hub-and-spoke analytics model coherent as it grows.
If you're excited by the space where data engineering, LLM tooling, and analytics governance meet, this role sits right in the middle of it.
What youll Own:
Snowflake semantic layer - Own the semantic layer end-to-end as the single source of truth for metrics; design semantic models, enforce naming standards, and ensure consistent metric definitions across dashboards and AI agents.
Cortex Agents - Design and deploy conversational AI agents using Cortex Analyst and Cortex Search; tune for accuracy and safety, expose through multiple surfaces (Snowflake Intelligence, Streamlit, MCP), and build evaluation harnesses to maintain quality at scale.
Data craft (Analytics guild) - Co-lead the technical track of the Analytics guild; set SQL and modeling standards, run technical enablement and code reviews, and serve as the technical authority for analysts.
Scaling data infrastructure - Improve performance, reliability, cost efficiency, and governance across the dbt/Airflow/Airbyte/Snowflake stack as data volume and query load grow; optimize warehouse sizing, medallion layers, and ingestion pipelines.
What you'll do day to day:
Model and maintain semantic views that power both dashboards and AI agents, keeping definitions versioned, tested, and certified.
Build, evaluate, and iterate on Cortex Agents - including retrieval quality, guardrails, and observability.
Extend and optimize dbt models, Airflow DAGs, and Airbyte connectors across bronze/silver/gold layers.
Partner with GTM, Finance, CS, and Product stakeholders to translate business questions into governed, reusable data assets.
Run the Data Analyst guild: standards, reviews, enablement, and tooling.
Own data quality, lineage, and cost monitoring across the Snowflake platform.
Expose data and agents through Streamlit apps, BI tools (Omni), and MCP servers for internal AI workflows.

Were looking for a Hands-on AI & Data Engineering Manager to lead a team building intelligent, large-scale consumer experiences powered by AI. In this role, you will lead engineers developing AI-driven products, work closely with data scientists, product managers, and designers, and ensure that AI capabilities-from LLMs and agents to product data insights-are translated into reliable, scalable production systems. You will be responsible not only for delivering AI features, but also for analyzing product data and user behavior to continuously improve AI performance and product outcomes. This is a technical leadership role combining engineering management, architecture ownership, AI product development, and data-driven decision making.
Key responsibilities:
Lead a team of data scientists, data engineers and product analysts
Own the delivery of AI-powered product capabilities, from research and experimentation to production and operation
Drive excellence, code quality, and best development practices
Provide technical direction and hands-on guidance for complex AI systems
Drive the integration of LLMs, AI agents, and intelligent workflows into core consumer experiences
Ensure AI solutions are safe, scalable, observable, and continuously improving
Lead initiatives around product data analysis and experimentation
Analyze user interactions with product features to improve accuracy, UX, and business impact
Partner with product teams to define metrics, dashboards, and experiments that guide product improvements
Design system architectures for AI-enabled applications at scale
Evaluate and select technologies for AI platforms and data pipelines
Guide the development of prompt engineering frameworks and centralized prompt management
Ensure robust monitoring, evaluation, and feedback loops for AI outputs
Translate product and business goals into technical roadmaps and execution plans
Drive alignment between AI capabilities and measurable product outcomes.

The AI Forward Deployed Engineer (FDE) is responsible for driving the successful adoption of AI solutions across our Groups business units.

The role focuses on translating business needs into practical AI solutions and ensuring they are effectively embedded into real workflows, delivering measurable business impact.

FDEs work closely with business teams to identify high‑value opportunities, deploy and build customized AI solutions, and continuously improve outcomes. Success is measured by adoption, usage, tech quality and business value.


Key Responsibilities
Partner with business units and tech teams to identify and prioritize AI use cases.
Own AI initiatives end‑to‑end: from problem definition to deployment and impact measurement.
Deploy and adapt AI solutions to fit real operational workflows.
Drive user adoption through training, iteration, and feedback.
Support continuous improvement and scaling of successful solutions.

1. AI Architecture & Technical Leadership

Guide AI architectural direction across platform, focusing on system design, model lifecycle, and integration of AI components into product workflows.
Act as a senior technical reviewer and thought partner for complex or cross-team AI design decisions.
Provide technical oversight on areas such as exploring new models/technologies/opportunities for the company
Surface architectural risks, tradeoffs, and long-term implications, including cost, security and compliance aspects and clearly advise the VP of AI when certain technical directions should not be pursued.
This role influences judgment, clarity, and experience, not through blocking authority.

2. Hands-on Applied Innovation (Core Pillar | ~50%)

Spend at least 50% of time hands-on, building:
End-to-end AI prototypes
Technical demos and proofs of concept
Exploratory implementations of new AI capabilities
Drive applied innovation that:

De-risks new technologies
Demonstrates feasibility and impact
Informs product direction and business opportunities
Build fast, concrete examples that teams can learn from and extend.
Transition successful prototypes to team ownership for further development and scaling.
This role is expected to lead AI innovation by doing, while working closely with product, medical and engineering

3. Best Practices & Technical Enablement

Define and promote best practices for applied AI development, including:
Rapid prototyping and vibe coding.
Agent design, orchestration, and evaluation patterns
Experimentation, benchmarking, and validation workflows
Help teams align on shared technical patterns, tools, and standards.
Identify opportunities to consolidate duplicated efforts and improve cross-team coherence.
Lead technical deep dives, architecture discussions, and design reviews.
4. AI Compliance & Regulatory Enablement (Technical Scope)

Ensure Navinas AI development practices align with applicable AI regulations for a software product handling sensitive medical data.
Define and guide AI-specific compliance practices, including data usage, transparency, evaluation, and documentation expectations.
Support and contribute to AI-related compliance and regulatory documentation, in close collaboration with Legal, Security, and Medical Research teams.
Serve as a technical point of reference for AI compliance questions.

We are seeking a Senior Software Architect to lead and shape the architectural vision of our SaaS healthcare platform, guide the adoption of cutting-edge AI technologies, and ensure our solutions remain scalable, secure, and future-ready. This is a hands-on, strategic leadership role that combines visionary system design, proof-of-concept development, and AI-first innovation enablement.

Responsibilities
1. Architectural Leadership

Define, communicate, and evolve the platforms architectural vision to ensure alignment with business goals.
Lead high-level design discussions and decisions for new and existing products.
Establish and maintain architectural principles, standards, and patterns across all engineering teams to ensure consistency and coherence.
Drive cloud-native architecture in AWS with a strong focus on scalability, availability, performance, and cost efficiency.
2. Innovation & Emerging Technology

Build POCs and pilots for new solutions, frameworks, and AI capabilities.
Evaluate emerging trends in AI, LLMs, MCPs, agents, and developer productivity tools, ensuring strategic adoption where relevant.
Initiate projects/features to solve widespread problems (including work with Support, DevOps, Infosec)
3. AI Tooling Evangelism

Spearhead engineering-wide adoption of AI-assisted development workflows, including developer productivity tools, code copilots, automated quality checks, and automated documentation.
Define best practices, guidelines, and governance for AI usage in development (e.g., Cursor configuration rules, prompt engineering standards).
Educate and mentor teams on effective AI utilization for engineering, product, and operational tasks, taking into account security, costs, and compliance.
4. Technical Governance & Best Practices

Overall ownership of the system scalability, COGS, and stability of the product(s)
Define standards for coding, testing, CI/CD, and monitoring.
Enforce architectural and design reviews for key initiatives.
5. Cross-functional Collaboration

Work closely with Engineering, Product, Data Science, DevOps, and Security teams to deliver well-architected solutions.
Communicate complex technical concepts to non-technical audiences, including executives and external partners.