Engineering - Agentic AI Engineer (Junior)

Responsibilities

• Build and deploy agentic systems for enterprise workflows — design and implement AI agents (and multi-agent systems) that reason and retrieve data across complex business processes and take action in enterprise systems.
• Design and ship multi-step agentic systems — planner/executor, tool-using, multi-agent, and human-in-the-loop — for use cases including onboarding, underwriting, case review, and continuous monitoring.
• Architect agent graphs in LangGraph (or comparable frameworks — CrewAI, AutoGen, Claude Agent SDK) with explicit state, durable execution, retries, and safe fallbacks.
• Expose agents to production systems via well-typed tools and MCP servers; treat the tool surface area as a product.
• Own full-stack implementation and integrations — build across LLMs, APIs, backend systems, and lightweight UIs to deliver complete, working solutions.
• Build and own the retrieval layer powering our agents: chunking strategies, hybrid search (vector + keyword), reranking, and grounded citation.
• Design and optimize embedding pipelines and vector indexes using pgvector and OpenSearch.
• Develop agentic harnesses to accelerate development — create evaluation frameworks, toolchains, and workflows that enable rapid iteration and improve system reliability.
• Own the eval stack: curate golden sets, maintain offline regression suites, implement LLM-as-judge, and run online A/B and shadow evals.
• Ensure reliability, safety, and production readiness — implement guardrails, validation logic, and fallback mechanisms to ensure consistent and trustworthy behavior in production.

Qualifications

• Educational background: Bachelor's degree in Computer Science, Data Science, AI/ML, or related field, or equivalent practical experience through projects, research, internships, or professional work.
• Experience: 1–5+ years in software engineering (full-stack or backend), or a strong recent graduate with demonstrable project or internship experience at equivalent depth.
• Technical skills: Proficiency in Python; hands-on experience with at least one LLM framework; familiarity with RAG architecture; working knowledge of SQL and relational databases; familiarity with Git version control and Agile/Scrum practices.