Senior Assistant Vice President

We are seeking a Lead Agentic AI Engineer with a strong software engineering foundation to design, build, and scale autonomous and semi‑autonomous AI systems . This role goes beyond prompt engineering or isolated models—you will architect multi‑agent systems that plan, reason, call tools, interact with enterprise systems, and operate safely at scale. You will work on agent orchestration, RAG pipelines, tool‑calling, memory, observability, and AgentOps , while ensuring production readiness, compliance, and reliability. Strong Software Engineering Background (Must Have) 8-10 years of experience with backend/software engineering Expert in Python (primary) and API‑based services Experience with distributed systems, microservices, async processing Strong understanding of system design, scalability, and performance Agentic AI & GenAI Expertise Proven experience building agentic AI systems (not just chatbots) Hands‑on with agent frameworks (e.g., LangChain / LangGraph / equivalent) Strong understanding of: Tool‑calling, planning, reflection Memory, state, and long‑running agents Experience working with LLMs (cloud and/or open‑source) RAG & Data Engineering Production experience with vector databases Knowledge of embeddings, retrieval strategies, reranking Comfortable with structured + unstructured enterprise data Platform & Ops Docker, CI/CD, cloud deployments (AWS / Azure / GCP) Experience with monitoring, logging, and production debugging Familiarity with MLflow or similar experiment tracking tools Nice to Have Experience in healthcare, insurance, or regulated domains Exposure to AI governance, compliance, or risk frameworks Built internal AI platforms or reusable accelerators Prior experience tech‑leading enterprise AI initiatives Agentic AI System Design Design and implement multi‑agent architectures including: Orchestrator / supervisor agents Task‑specialized agents (research, extraction, validation, decisioning) Reflection, critique, and self‑correction loops Build goal‑driven agent workflows with constrained autonomy and human‑in‑the‑loop patterns. Define agent boundaries, decision policies, and escalation logic.

LLM, RAG

& Tooling Build enterprise‑grade RAG systems : Ingestion, chunking, metadata enrichment Vector indexing and retrieval strategies Grounded generation and citation control Implement tool‑calling agents that interact with: APIs, databases, search systems Internal platforms and workflows Optimize latency, cost, and accuracy across LLM interactions. Production Engineering & AgentOps Build agentic systems as scalable backend services (Fast

API / REST

/ async services). Apply strong software engineering discipline : Modular code, clean abstractions, testability CI/CD, versioning of prompts, tools, and agents Implement AgentOps / LLMOps , including: Evaluation harnesses (prompt, retrieval, agent behavior) Observability (traces, metrics, decision paths) Rollback and controlled rollout strategies Ensure robustness against hallucinations, loops, tool failures, and unsafe actions. Governance, Safety & Reliability Implement guardrails, policies, and monitoring for agent behavior. Design systems with traceability, auditability, and explainability . Ensure secure handling of sensitive data (PII/PHI where applicable). Enforce responsible‑AI principles in autonomous systems. Technical Leadership Lead design reviews and mentor junior engineers. Influence platform standards for agentic AI across teams. Partner with product managers, architects, and domain SMEs to translate workflows into agent behavior.