Full-Stack AI Engineer (ML/DL, GenAI, LLMs, Agentic AI)

Full-Stack AI Engineer (ML/DL, GenAI, LLMs, Agentic AI)

About the role

Were building intelligent systems that move from insight to action—at scale. As a Full-Stack AI Engineer, you’ll design, ship, and own AI products that blend cutting-edge research (LLMs, Generative AI, Agentic workflows) with real-world impact. You’ll work end-to-end: from data pipelines and model training to robust APIs, delightful UIs, and cloud-native deployments that serve thousands of users reliably.

What you’ll do

• Design, train, and ship ML/DL/GenAI models (LLMs, diffusion, retrieval-augmented generation) for production use cases.
• Build agentic systems: autonomous/goal-driven agents, tool-use, planning, memory, and multi-step orchestration.
• Own the full stack: data ingestion/ETL feature stores model serving backend APIs (REST/GraphQL) lightweight frontends/SDKs.
• Operationalize models: containerize (Docker), orchestrate (Kubernetes), instrument monitoring (latency, drift, hallucinations), and automate CI/CD.
• Collaborate cross-functionally with Product, Design, Data, Security, and Compliance to refine requirements, experiment, and iterate quickly.
• Scale systems in the cloud (Azure): design for reliability, observability, and cost-efficiency.
• Champion quality & safety: evaluation harnesses, red-team testing, guardrails, prompt/finetune hygiene, and privacy-by-design.
• Document and mentor: write clear specs, contribute to internal tooling, and share best practices.

Tech you’ll use (and/or quickly learn)

Languages:

Frameworks:

LLM/GenAI stack:

MLOps:

Cloud/Infra:

Security/Compliance:

What you bring

• 5 years building production systems, including 3+ years with ML/DL and hands-on experience with LLMs/GenAI.
• Proven track record deploying models to production and supporting them (SLOs, on-call, post-mortems).
• Strong software engineering foundations: testing, code reviews, design patterns, performance profiling.
• Experience designing RAG/agentic pipelines, tool integration (function calling), and evaluation frameworks.
• Comfortable with ambiguous problem spaces; able to turn business goals into measurable technical plans.
• Soft skills: ownership mindset, crisp communication, collaborative spirit, bias for action, and a product mindset.

Nice-to-have

• Advanced degree in CS/EE/Stats/Applied Math—or equivalent applied experience.
• Domain expertise in fintech, risk, fraud detection, document understanding, or workflow automation.
• Experience with streaming data, graph ML, reinforcement learning, or multimodal (text-vision-audio).
• Contributions to open-source AI or published research.