דרושים » AI » AI-Data Engineer

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.
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.

We are looking for an experienced and visionary Data Engineer to help design, build, and scale our BI platform.
In this role, you will be responsible for developing our data analytics platform - enabling scalable data pipelines and robust data modeling to support real-time and batch analytics to provide insights that serve both business intelligence and product needs.
You will be part of the R&D team, collaborating closely with engineers, analysts, and product managers to deliver a modern data architecture that supports internal dashboards and future-facing operational analytics.
If you enjoy turning raw data into powerful insights and owning the full data lifecycle, this role is for you!
Responsibilities:
Take full ownership of the design and implementation of a scalable and efficient BI data infrastructure, ensuring high performance, reliability, and security.
Design and architect data products, from ingestion to transformation, modeling, storage, and access.
Build and maintain ETL/ELT pipelines, batch and real-time, to support analytics, reporting, and product integrations.
Establish and enforce best practices for data quality, lineage, observability, and governance to ensure accuracy and consistency.
Integrate modern tools and frameworks such as Airflow, Databricks, Power BI, and streaming platforms.
Collaborate cross-functional with product, engineering, and analytics teams to translate business needs into data infrastructure.
Promote a data-driven culture - be an advocate for data-driven decision-making across the company by empowering stakeholders with reliable and self-service data access.

We are looking for a Senior Data Engineer to build and scale the data foundation behind platform and products. You will own complex data end-to-end - from ingestion and transformation through modeling, quality, observability, and production delivery.
This is a hands-on senior IC role for a strong builder who can solve difficult data problems independently, set a high technical bar, and collaborate closely with engineering, DS, and product. You will turn large, fragmented datasets into reliable, reusable capabilities that power every product.
Responsibilities
Build and own scalable data pipelines- Design, implement, and operate robust pipelines for high-volume structured and unstructured data, with validation, monitoring, lineage, and recovery built in.
Scale the platform for growth- A key near-term initiative is re-architecting the system to support a significantly larger customer base. You will own performance and cost-efficiency across pipelines and services, keeping reliability and operating costs under control as the platform scales.
Build across the stack- This is not a pipelines-only role. You will also write backend services and some frontend, including the internal backoffice the team runs on. We hire builders, not narrow specialists.
Own the core data tables- Own schema design and evolution, data contracts, and the modeling standards the team follows - naming, shared dimensions, normalization, documentation. Be accountable when a table is wrong, late, or drifting.
Level up the teams data work- Pair with and advise software engineers and data scientists on Spark, SQL, and modeling, and help turn notebook-grade code into production-grade pipelines.
Partner cross-functionally- Translate product, client, compliance, and business requirements into clear technical designs and dependable production systems.

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 company 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
Design and oversee API strategies, event-driven architectures, and middleware patterns that support scalable AI feature delivery - including agentic workflows, intelligent data transformation, anomaly detection, and natural language interfaces layered onto ERP and CRM data
Collaborate with Engineering during build phases, conducting architecture reviews, providing hands-on guidance, and resolving complex technical blockers
Define non-functional requirements - latency, security, auditability, model drift monitoring - for AI components embedded in mission-critical business processes
Establish MLOps and LLMOps practices appropriate for our enterprise environment: model versioning, observability, and rollback procedures for production AI workloads

We are looking for a Data Engineer to help build and scale the data platform behind our search quality, ML pipelines, product analytics, and business operations.
In this role, you will contribute across the full data lifecycle: ingesting data from production systems, designing and evolving our data warehouse, building batch and streaming pipelines, and making high-quality datasets available to researchers, engineers, analysts, and product teams across the company.
The platform spans tens of terabytes and ingests data from tens of proprietary and third-party sources - including our search engine and its components, CRM, billing, identity, and product analytics across multi-region production environments. Around 100 internal users rely on it daily.
You will work closely with engineers and stakeholders across the company, contribute to architectural and modeling decisions, and help improve the reliability, usability, and scalability of the data platform as it grows.

In this position, your responsibility will be to:
Contribute to the design, development, and operation of Tavily's data platform - from real-time ingestion through data warehouse medallion layers to consumer-facing datasets and dashboards.
Build and maintain reliable batch and streaming pipelines that ingest data from production services and external systems.
Design and evolve scalable, analytics-ready data models in the data warehouse.
Work closely with engineers across the company to ensure data produced by production systems is reliable, well-structured, and usable downstream.
Improve observability across the data platform, including data quality checks, freshness monitoring, lineage, schema evolution, and cost controls.
Partner with researchers, engineers, analysts, finance, and product managers to deliver trustworthy datasets for product, search quality, ML, and GTM analytics.
Contribute to defining the objects, entities, and relationships that represent Tavily's search domain - including agent inputs, URLs, chunks, agent sessions, crawls, and the connections between them - and translate them into clean, queryable data models.
Improve engineering practices around testing, documentation, deployment, and incident response.
Investigate and resolve production data issues, including broken pipelines, corrupted datasets, schema changes, and large-scale backfills.
Contribute to technical standards and best practices for data engineering across the company.
Help maintain high standards of data quality, integrity, security, and governance across environments.

We are a well-funded, early-stage startup looking for a talented and motivated Backend Engineer specializing in infrastructure to join our founding team. The focus of this role is to build and scale the infrastructure that powers autonomous AI agents automating complex enterprise workflows. You will own the systems, pipelines, and platforms that let our AI agents run reliably, securely, and at scale in production.

Your Impact
Infrastructure & Platform

Design, build, and own the core infrastructure powering our AI agent platform, from data pipelines to production deployment systems.

Build and scale the backend systems that support high-throughput document processing and data extraction workloads.

Cloud Infrastructure and Scalability

Architect and deploy infrastructure on cloud platforms (AWS, GCP, or Azure) with a focus on scalability, reliability, and cost efficiency.

Own containerization and orchestration (Docker, Kubernetes) for all production workloads.

Build and maintain CI/CD pipelines and DevOps practices that let the team ship fast without breaking things.

Data Infrastructure

Design and manage data pipelines to process and analyze large volumes of documents and unstructured data at scale.

Build the infrastructure layer connecting AI agents to databases, vector stores, and enterprise systems (ERP, CRM).

API & Systems Integration

Build and maintain robust, well-documented APIs connecting AI agents with external systems and enterprise software.

Design for reliability: retries, observability, and graceful degradation across distributed systems.

Security and Compliance

Implement authentication and authorization mechanisms (OAuth2, JWT) to secure AI-driven systems.

Ensure compliance with data privacy standards (e.g. GDPR, HIPAA) and drive best practices for secure data handling across the infrastructure.

Monitoring and Optimization

Build observability and monitoring systems to track infrastructure health, performance, and cost.

Continuously optimize system performance for speed, reliability, and cost-efficiency at scale.

Collaboration

Work closely with AI/ML engineers, product, and the founding team to make sure infrastructure decisions support fast iteration and production-grade reliability.

Participate in code reviews, design discussions, and architecture planning to drive infrastructure strategy.

As an AI Expert at our company, youll take full ownership of AI-powered product capabilities - from concept to production. Youll work closely with Product, Engineering, and Security domain experts to build practical, reliable AI systems that improve how teams operate and secure cloud environments. This is an applied role: we care about shipping, adoption, and measurable outcomes. Youll engage with users, test MVPs in production, iterate quickly based on feedback, and make sure what you build holds up in the real world. And yes youll also jump in as a Support Hero when needed, helping customers succeed and learning directly from how they work.
Key Responsibilities
Build and ship AI features that improve cloud security operations (e.g., control recommendations, drift detection explanations, guided remediation, policy generation, investigations, workflow automation).
Design and implement AI agents tuned for cloud-security tasks, including the surrounding framework (tools, permissions, orchestration, guardrails, and automation).
Apply deep understanding of cloud security domains (IAM, network controls, logging/telemetry, posture management, governance, misconfiguration, threat scenarios) to guide product decisions and ensure the AI behaves safely and correctly.
Integrate with multi-cloud APIs and security controls (AWS/Azure/GCP/OCI), building end-to-end flows from user intent → actionable outcome. Create and maintain evaluation methods for model quality (accuracy, coverage, hallucination rates, latency, cost, and user satisfaction) and build tooling to continuously measure performance in production.
Build data and feedback loops to keep systems relevant over time (user corrections, outcome tracking, regression testing, prompt/model iteration).
Stay current with practical advancements in LLMs/agents and engineering patterns, and adopt whats useful (not hype) into production.

We are seeking talented and passionate Senior Data Engineer to join our Data team. In this pivotal role, you will be instrumental in designing, building, and optimizing the critical data infrastructure that underpins innovative creative intelligence platform. You will tackle complex data challenges, ensuring our systems are robust, scalable, and capable of delivering high-quality data to power our advanced AI models, customer-facing analytics, and internal business intelligence. This is an opportunity to make a significant impact on our product, contribute to a data-driven culture, and help solve fascinating problems at the intersection of data, AI, and marketing technology.

Key Responsibilities
Architect & Develop Data Pipelines: Design, implement, and maintain sophisticated, end-to-end data pipelines for ingesting, processing, validating, and transforming large-scale, diverse datasets.
Manage Data Orchestration: Implement and manage robust workflow orchestration for complex, multi-step data processes, ensuring reliability and visibility.
Advanced Data Transformation & Modeling: Develop and optimize complex data transformations using advanced SQL and other data manipulation techniques. Contribute to the design and implementation of effective data models for analytical and operational use.
Ensure Data Quality & Platform Reliability: Establish and improve processes for data quality assurance, monitoring, alerting, and performance optimization across the data platform. Proactively identify and resolve data integrity and pipeline issues.
Cross-Functional Collaboration: Partner closely with AI engineers, product managers, developers, customer success and other stakeholders to understand data needs, integrate data solutions, and deliver features that provide exceptional value.
Drive Data Platform Excellence: Contribute to the evolution of our data architecture, champion best practices in data engineering (e.g., DataOps principles), and evaluate emerging technologies to enhance platform capabilities, stability, and cost-effectiveness.
Foster a Culture of Learning & Impact: Actively share knowledge, contribute to team growth, and maintain a strong focus on how data engineering efforts translate into tangible product and business outcomes.

We are looking for a strong Backend Software Engineer to bridge the gap between our Machine Learning research team and our enterprise production systems. You will act as the technical backbone for our ML Scientists - by advising, designing and implementing the production facing features. If you are a backend expert who wants to solve complex system architecture challenges and dive into the world of ML platforms & Agentic LLM pipelines, this is the role for you - An exciting role collaborating with ML science team, data/infra team and DevOps to drive real customer impact.



As a ML Engineer, you will:



Lead ML delivery: transforming research output (code, models, ideas) into robust, scalable, low-latency microservices in production

Help architect e2e solutions to real customer pains ranging from ingestion, integration, ETLs, DB design up to low-latency services

Design, build, and maintain automated workflows for ML models, including auto-trains, benchmarking, testing, performance gating, and production deployment.

Tackle complex backend challenges: optimizing API response times, managing database connectivity and concurrency at scale, balancing accuracys drive for complex questions with the business needs of fast responsiveness by making hard technical trade-offs between customer gains and business costs.

Design and optimize data pipelines and ETL processes, connecting our Snowflake data warehouse to our training environments.

Work within our existing ML infrastructure (Kubeflow, MLflow, KServe) to ensure smooth model lifecycles and performance monitoring.

Collaborate closely with ML Scientists, guiding them on software engineering best practices without slowing down their research.

Monitor and optimize production models for performance, cost efficiency, availability, and observability.

We are looking for a Senior Data Architect to help define and evolve the data architecture of our SaaS platform and core products. You will work closely with product, delivery, and engineering squads to design scalable, reliable data systems, set technical direction for how data is ingested, modeled, stored, and served, and guide teams in making high-quality architectural decisions that balance delivery speed with long-term sustainability.
This is a hands-on, senior technical leadership role: you will spend most of your time shaping data architectures with teams, reviewing data models, pipelines and critical code, and driving shared standards and patterns across the organization, all in an advanced agentic environment.
Responsibilities:
Define and evolve the data architecture for key domains (ingestion, ELT/ETL, data modeling, storage, APIs, integration, observability, governance, and security), including owning the lakehouse/medallion architecture (bronze/silver/gold) and the data flows that move data across layers at scale.
Translate business and product requirements into pragmatic data designs, data contracts, and architecture roadmaps; create and maintain architecture artefacts (data flow/lineage diagrams, ADRs, reference implementations, modeling guidelines).
Evaluate design options and technology choices, articulate trade-offs, and lead decision-making with stakeholders; push forward the agentic mindset and implementation across the data platform.
Partner with squad leads and senior engineers to design data solutions, break down complex problems, and keep implementations aligned with the target architecture; participate in design/tech reviews to ensure NFRs (performance, scalability, data quality, resilience, security, operability) are addressed early.
Provide hands-on support where it matters most: spike and prototype critical data flows, review complex PRs, and help debug tricky production data and pipeline issues.
Collaborate with Product to shape technical feasibility, sequencing, and scope for data-driven roadmap items; communicate complex technical topics in simple language to non-technical stakeholders.
Define and promote data architecture principles, modeling conventions, coding standards, and reusable patterns (shared transformations, libraries, datasets, services) to reduce duplication and technical debt; drive adoption of shared platform capabilities (observability, CI/CD, orchestration, governance, DevOps tooling) across squads.
Ensure data architectures are observable, operable, and resilient by design; partner with DevOps/SRE on deployment, monitoring, data quality, and incident response; identify areas of high technical debt or architectural risk and lead remediation initiatives; define and track technical KPIs tied to architecture decisions.
Act as a technical mentor for senior engineers and tech leads, fostering a culture of thoughtful design, documentation, and constructive technical debate; lead by influence- help teams make better decisions instead of making every decision for them.