Agentic AI Data Scientist/Engineer 1

Lead to head up the team responsible for designing, building, and deploying agents and multi-agent workflows on our client's Multi-Agentic Platform. You'll lead a group of engineers/data scientists across the full agent lifecycle orchestration, LLM and prompt engineering, system integration, and evaluation while staying hands-on with architecture and technical direction.

This role sits at the intersection of applied AI engineering and data science, and requires someone who can both lead a team and dive deep into agent design, orchestration frameworks, and LLM behavior.

Required Qualifications :

- 8+ years in software/ML engineering or data science, including recent hands-on experience building LLM-powered or agentic systems in production.

- Strong Python engineering skills production-quality code, testing, packaging, and API design.

- Hands-on experience with agent orchestration frameworks, especially LangGraph (LangChain, AutoGen, CrewAI, or Semantic Kernel also relevant).

- Deep understanding of LLM fundamentals: prompt engineering, context management, tool/function calling, RAG, embeddings, and model selection tradeoffs.

- Experience designing and implementing evaluation frameworks for LLM/agent outputs (offline eval sets, human-in-the-loop review, automated scoring/LLM-as-judge techniques, regression testing).

- Solid data science fundamentals statistics, experimentation, model evaluation methodology and ability to apply them to non-deterministic AI systems.

- Prior experience leading a team of engineers or data scientists technical mentorship, code/design review, and delivery ownership.

- Experience integrating AI systems with external APIs, databases, and enterprise data sources.

- Strong communication skills able to explain agent architecture and tradeoffs to both technical teams and business stakeholders.

Preferred Qualifications :

- Experience with multiple LLM providers/APIs (OpenAI, Anthropic, AWS Bedrock, Azure OpenAI) and model routing/fallback strategies.

- Experience with vector databases and RAG pipelines (e.g., Pinecone, OpenSearch, pgvector, FAISS).

- Familiarity with AWS-based deployment of AI workloads (Lambda, ECS/EKS, SageMaker, Bedrock).

- Experience building observability/tracing tooling for agentic systems (e.g., LangSmith, custom tracing, OpenTelemetry).

- Background in consulting or client-facing delivery environments, managing scope and stakeholder expectations.

- Experience with multi-agent design patterns (planner/executor, supervisor/worker, hierarchical agents, tool-routing agents).

- Prior experience partnering with front-end/UI engineering teams to expose agent configuration and monitoring through a self-service interface.