דרושים » הנדסה » ML Engineer

we are looking for a AI Architect.
Responsibilities:
1. AI Architecture & Technical Leadership
Guide AI architectural direction across Navinas 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're looking for an AI Engineer who is equal parts builder, enabler, and visionary.
This is a rare opportunity to join a small, elite team at the ground floor and have outsized impact on how AI is designed, built, and shipped across a globally recognized cybersecurity platform.
If you thrive at the intersection of cutting-edge AI research and real-world production systems and you want your fingerprints on something that matters - read on.
Why Join Us?
Greenfield opportunity - you're not joining a mature team with fixed patterns, you're helping define them.
Real impact at scale - your work will influence products used by thousands of organizations worldwide.
A team of great people - small, senior, and genuinely collaborative.
Freedom to innovate - we encourage bold ideas, fast experiments, and honest feedback.
our company's AI moment - AI is a company-wide strategic priority, and this group is at the center of it.
*we are an equal opportunity employer committed to diversity and inclusion.
Key Responsibilities
What You'll Do:
Build AI infrastructure - Design and develop the foundational tools, frameworks, and pipelines that power the group's AI capabilities, with a focus on LLMs and Generative AI.
Enable AI across the team - Act as the group's AI enablement engine: establish best practices, create internal tooling, and uplift teammates to work effectively with AI systems.
Own AI agents & agentic workflows - Design, implement, and iterate on autonomous agents and multi-step AI pipelines integrated with a variety of tools and environments.
Bring AI to production - Take models and capabilities from prototype to production-grade systems - reliable, scalable, and observable.
Shape the big picture - Contribute to the group's AI strategy, not just its execution. We want someone who asks "why" before diving into "how."
Stay ahead of the curve - Continuously research and evaluate emerging AI techniques, models, and tools - and bring what's relevant back to the team.
Collaborate and communicate - Write clearly. Think clearly. Work closely with researchers, engineers, and product stakeholders to align on goals and drive outcomes.

You will manage the AI Platform Engineer(s), set the technical standards for the AI Power User group's citizen development program, and serve as the connective tissue between business leadership, platform owners, and development teams. You will shape the multi-year AI architecture roadmap while also rolling up your sleeves to conduct architecture reviews, resolve blockers, and move use cases from concept to production. This is a role for someone who can think big and execute - and who understands that in an enterprise context, the quality of your governance is inseparable from the quality of your architecture.
What You'll Own
Strategy & Architecture
Define and own the enterprise AI integration strategy - identifying opportunities to embed intelligent automation, agentic workflows, predictive analytics, and generative AI capabilities across our core platforms
Develop and maintain reference architectures, design patterns, and the AI architecture decision log that governs how AI models connect to enterprise systems and what they are permitted to do
Consult on enterprise system architecture and implement best practices for the Enterprise Business Systems team to leverage in their day-to-day execution.
Lead Proof-of-Concept initiatives for new AI tools and platform-native AI features, evaluating them against build-vs-buy criteria before recommending adoption
Partner with business stakeholders to translate operational pain points into AI use cases with clear ROI framing and sequencing criteria
Contribute to our enterprise data strategy, ensuring AI initiatives are supported by clean, accessible, and well-governed data pipelines
Integration Architecture & Delivery

Design and own the Workato eMCP layer - the MCP governance model, persona-scoped token framework, workspace isolation strategy, and the single sanctioned action surface through which all AI agents write back to enterprise systems
Define integration patterns and standards for AI model connectivity (Claude, ChatGPT) to Salesforce, NetSuite, HiBob, and Jira - specifying what agents can read, what they can write, through which surfaces, and with what confirmation and audit requirements

We are looking for a hands-on, business-minded AI Engineer to join our Data & AI Team.
This role is for someone who has already built and shipped real AI products in a company environment. You will work as an integral part of the Data & AI Team, partnering directly with business stakeholders to identify high-impact opportunities, translate business needs into technical solutions, and build AI products that automate internal processes and create immediate value.
The ideal candidate is a builder. You should be comfortable working with LLMs, AI agents, automation workflows, APIs, data pipelines, data warehouses, and internal company systems. You should also be comfortable taking ownership, asking sharp business questions, and moving ideas from concept to production.
Our Data Team has already built internal AI products at our company. Now we are looking for someone who can help take us to the next level.
What Were Looking For
The right person has built with AI in a real company environment and knows how to turn business needs into practical internal products. They should be comfortable working with stakeholders, understanding how teams operate, and identifying where AI can create meaningful value.
Because this role sits inside the Data & AI Team, they also need to be strong with data. That means working confidently with company data, data warehouses, pipelines, APIs, and the technical building blocks that make AI products reliable and useful.
This role is for someone hands-on, curious, and hungry to build. Someone who can combine AI, data, and business context to help us move faster, automate smarter, and turn ideas into measurable business wins.
What Youll Do
Build end-to-end internal AI products, automations, agents, and workflows that solve real business problems across our company.
Work directly with business stakeholders to understand pain points, define requirements, and turn ideas into scalable AI-driven solutions.
Design, prototype, test, deploy, and maintain production AI products using LLMs, AI agents, APIs, automation frameworks, and internal company data.
Work hands-on with data tools and infrastructure, including Snowflake, ETLs, data pipelines, APIs, AI tools, and internal servers.
Identify high-impact manual processes and turn them into automated AI-driven business wins.
Evaluate new AI tools, frameworks, and agentic workflows, and apply them where they can improve productivity, decision-making, or business operations.
Help shape internal AI development best practices around reliability, usability, security, documentation, maintainability, and production readiness.

We are looking for a Hands-On Team Lead - Agentic Engineering to lead, build, and actively contribute to a new team focused on applying AI agents, and intelligent workflows to improve business processes across the organization.
This role is both a technical leadership role and a hands-on engineering role. The successful candidate will manage the first Agentic Engineer, help grow the team, and personally contribute to the design, development, integration, and deployment of agentic AI solutions.
The ideal candidate is someone who can lead from the front: writing code, designing architectures, building prototypes, reviewing technical work, solving complex implementation challenges, and working directly with business stakeholders to turn process opportunities into working AI-driven solutions.
Responsibilities:
Partner directly with business teams to identify automation and optimization opportunities
Design and implement agent-based AI workflows to automate internal processes end-to-end
Design and build LLM-powered tools (agents, workflows, copilots)
Develop RAG pipelines, integrate multiple data sources, and build intelligent automation flows
Deep-dive into company data - validate quality, uncover gaps, and ensure AI solutions are built on solid foundations
Take solutions from idea → prototype → production
Governance, Reliability & Security
Ensure AI workflows comply with security, privacy, and compliance requirements
Implement guardrails, approvals, logging, and human-in-the-loop mechanisms where needed
Monitor AI performance, errors, hallucinations, and drift
Collaboration & Enablement:
Partner with business owners and IS teams to identify automation opportunities
Translate business requirements into AI-driven solutions
Document AI flows, decision logic, and operational runbooks
Educate internal teams on AI capabilities and limitations.

We are looking for a Hands-On Agentic AI Engineer to join a new team focused on applying AI agents and intelligent workflows to improve business processes across the organization.
The ideal candidate is someone who enjoys building real-world AI-driven solutions end-to-end - writing code, designing architectures, building prototypes, solving implementation challenges, and working directly with business stakeholders to turn process opportunities into working AI solutions.
Responsibilities:
Partner directly with business teams to identify automation and optimization opportunities
Design and implement agent-based AI workflows to automate internal processes end-to-end
Design and build LLM-powered tools (agents, workflows, copilots)
Develop RAG pipelines, integrate multiple data sources, and build intelligent automation flows
Deep-dive into company data - validate quality, uncover gaps, and ensure AI solutions are built on solid foundations
Take solutions from idea → prototype → production
Governance, Reliability & Security
Ensure AI workflows comply with security, privacy, and compliance requirements
Implement guardrails, approvals, logging, and human-in-the-loop mechanisms where needed
Monitor AI performance, errors, hallucinations, and drift
Collaboration & Enablement:
Partner with business owners and IS teams to identify automation opportunities
Translate business requirements into AI-driven solutions
Document AI flows, decision logic, and operational runbooks
Educate internal teams on AI capabilities and limitations.

Were looking for a hands-on Agentic AI Engineer to build practical, high-impact AI solutions that transform how our teams operate.
This is not a research role. Youll design and ship real AI systems that improve workflows across Sales, Marketing, HR, and Operations - building custom solutions where off-the-shelf tools fall short.
If you enjoy turning messy business problems into production-ready AI systems using LLMs,SLMs, agents, and smart data pipelines - this role is for you.
Responsibilities:
Partner directly with business teams to identify automation and optimization opportunities
Design and implement agent-based AI workflows to automate internal processes end-to-end
Design and build LLM-powered tools (agents, workflows, copilots) using frameworks like LangGraph / LangChain
Develop RAG pipelines, integrate multiple data sources, and build intelligent automation flows
Deep-dive into company data - validate quality, uncover gaps, and ensure AI solutions are built on solid foundations
Take solutions from idea → prototype → production
Governance, Reliability & Security
Ensure AI workflows comply with security, privacy, and compliance requirements
Implement guardrails, approvals, logging, and human-in-the-loop mechanisms where needed
Monitor AI performance, errors, hallucinations, and drift
Collaboration & Enablement:
Partner with business owners to identify automation opportunities
Translate business requirements into AI-driven solutions
Document AI flows, decision logic, and operational runbooks
Educate internal teams on AI capabilities and limitations.

We are seeking a Senior AI Engineer to join a talented and fast-moving AI Engineering team. We're the central hub for AI across, driving innovation and putting AI to work for every team and product in the company.
AI already plays a foundational role in how we operate. It lets us ship features quickly and at scale, equips non-technical team members with tools that boost their productivity, and underpins much of the innovation happening across the company today.
If this kind of environment excites you - and if you believe great engineering means crafting the most effective and elegant solution within real-world constraints - we'd love to hear from you.
What You'll Be Doing:
Design, build, and maintain reusable AI capabilities - spanning models, tools, APIs, and platforms that support both internal operations and customer-facing products.
Develop and maintain our internal MCP server, giving AI agents easy and secure access to our extensive data stores.
Create and implement rigorous evaluation frameworks and AI guardrails to protect our value and ensure model reliability.
Cultivate deep expertise and establish sustainable AI engineering practices across the organization.
Champion AI readiness and track adoption company-wide to build lasting impact.
Build and optimize RAG (Retrieval-Augmented Generation) systems.
Own projects end to end - from gathering requirements from non-technical stakeholders through development, deployment, and ongoing operation.
Serve as a consultant and advocate for AI engineering, guiding other teams in leveraging the platforms and tools you create.
Collaborate with teams throughout to accelerate AI adoption and productization.

we are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
"our company's data management vision is the future of the market."- Forbes
we are the data platform company for the AI era. We are building the enterprise software infrastructure to capture, catalog, refine, enrich, and protect massive datasets and make them available for real-time data analysis and AI training and inference. Designed from the ground up to make AI simple to deploy and manage, our company takes the cost and complexity out of deploying enterprise and AI infrastructure across data center, edge, and cloud.
Our success has been built through intense innovation, a customer-first mentality and a team of fearless workers who leverage their skills & experiences to make real market impact. This is an opportunity to be a key contributor at a pivotal time in our companys growth and at a pivotal point in computing history.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

Required Senior AI Platform Engineer - Sovereign AI Engineering
The Dream Job
It starts with you - an engineer driven to build the agentic AI platform that turns LLMs into reliable, production-grade capabilities. You care about clean APIs, well-defined service boundaries, and systems that teams can build on with confidence. We are AI-first across the board - every team builds and operates agents. You'll architect and ship the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation pipelines, tool-calling infrastructure, and retrieval systems. Without this platform, agents don't ship - you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments.
If you want to make a meaningful impact, join our mission and build the agentic AI platform that drives Sovereign AI products - this role is for you.
Responsibilities
Design and build agentic systems - single and multi-agent workflows with planning, memory, context engineering, and tool use - for both internal automation and product-facing autonomous capabilities operating over long time horizons.
Build and operate the AI platform layer - LLM gateways, prompt management, structured output handling, tool-calling infrastructure, and cost/latency optimization - deployed on Kubernetes, consumed by every team for their agentic work.
Own the agent framework layer - orchestration primitives, execution environments, state management, and sandboxed tool execution - giving every team the building blocks to create and operate their own agents.
Build evaluation infrastructure that gives teams confidence in agent behavior - automated LLM and agent evals for quality, correctness, safety, latency, cost, and regressions, including human-in-the-loop oversight for mission-critical workflows.
Productionize and harden backend services (APIs, gRPC, async workers) that integrate LLMs - with proper error handling, retries, circuit breakers, and high-availability patterns.
Own RAG pipelines and retrieval systems - indexing, chunking, embedding, vector database management, filtering, and relevance tuning for production retrieval.
Optimize performance and cost across the AI stack - model routing, caching, batching, and inference cost management.
Ship shared tooling - libraries, SDKs, agent templates, and documentation - while working closely with ML Platform, Data Platform, DevOps, and other teams across the Applied AI Engineering group. Own architecture, documentation, and operations end-to-end.