Lead Agentic AI Developer

About the Role

The Principal Agentic AI Developer serves as the technical authority for designing, scaling, and governing autonomous AI systems across the enterprise. This leader architects multi‑agent ecosystems, establishes enterprise‑wide agentic patterns, and drives the adoption of AI‑driven automation that can reason, plan, and take action safely. The role blends deep LLM expertise, systems design, and strategic influence to deliver high‑impact, production‑grade autonomous capabilities.

• Architect enterprise-scale agentic systems including planners, executors, multi‑agent collaboration patterns, and long‑running autonomous workflows.
• Define the technical vision and roadmap for agentic automation, including frameworks, standards, governance, and best practices.
• Lead design of complex tool‑use ecosystems (APIs, RPA, MCP tools, knowledge systems, workflow engines) enabling agents to take real‑world actions.
• Oversee production deployment of autonomous agents with SLAs, observability, monitoring, and continuous evaluation.
• Partner with security, compliance, and legal teams to implement responsible AI guardrails, risk controls, and auditability.
• Mentor engineering teams on agentic design patterns, LLM orchestration, and safe deployment practices.
• Drive cross‑functional alignment with product, data, engineering, and operations to identify high‑value automation opportunities.
• Evaluate and integrate emerging technologies (MCP, Lang Graph, Semantic Kernel, Auto Gen, vector databases, knowledge graphs).
• Lead incident response and reliability engineering for agentic systems, ensuring safe fallback behaviors and human‑in‑the‑loop controls.

• 8–12+ years in software engineering, ML engineering, or AI systems development.
• Deep expertise with LLMs, RAG, embeddings, vector stores, and agent orchestration frameworks.
• Proven experience designing complex distributed systems, workflow engines, or automation platforms.
• Strong proficiency in Python, Type Script, or C# with production‑grade engineering practices.
• Hands‑on experience with Azure, AWS, or GCP, including secure deployment patterns.
• Demonstrated ability to lead architecture decisions, influence senior stakeholders, and drive enterprise adoption.
• Strong understanding of AI safety, governance, and risk mitigation strategies.
• Experience building evaluation harnesses, telemetry pipelines, and reliability frameworks for autonomous systems.

• Experience with MCP tool ecosystems, agent memory architectures, or long‑context models.
• Background in knowledge engineering, graph systems, or event‑driven architectures.
• Experience with Databricks, Microsoft Fabric, or large‑scale data platforms.
• Prior leadership of multi‑agent systems, hierarchical planners, or reinforcement learning agents.
• Experience guiding organizations through AI transformation or automation modernization.

• Competitive & Comprehensive Healthcare Package (available only for W2 hourly consultants)
• Simple IRA with company match (available only for W2 hourly consultants)
• Professional development & networking opportunities
• A family‑friendly environment
• Nice bonuses for referrals
• A culture that supports you and your career