Lead Software Engineer - Agentic AI

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Define and drive the platform roadmap for agent-based capabilities, focusing on measurable outcomes, reliability, and usability
• Lead end-to-end delivery of core agent platform components, including software development kits, reference implementations, and integration patterns
• Partner with product, engineering, risk, and control stakeholders to align requirements, prioritize trade-offs, and unblock execution
• Establish quality, performance, and operational standards for agent workloads, including monitoring, testing, and incident readiness
• Translate experimentation into production by driving clear architecture decisions, scalable designs, and repeatable deployment practices
• Guide responsible development practices by embedding governance, privacy, and model risk considerations into platform design
• Communicate technical strategy and progress to senior stakeholders with clarity, data, and pragmatic recommendations
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience 

• Proficiency in Python (primary for agent orchestration and LLM tooling) and/or TypeScript / Java / Go for enterprise backend integration.

• ​Data & RAG Systems: Designing hybrid search pipelines (dense vector retrieval, BM25, rerankers) paired with vector databases like Pinecone, Milvus, Qdrant, or pgvector.

• ​Backend & API Design: Building scalable microservices using FastAPI, Spring Boot, or Node.js to expose agent interfaces (REST, WebSockets, Server-Sent Events for streaming tokens and tool calls).

• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

• Agentic Frameworks & Orchestration: Production experience with multi-agent and workflow orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI, LlamaIndex Workflows, Semantic Kernel).

• ​Tool Calling & Function Calling: Deep expertise in structuring model tool calls, JSON schema validation, dynamic API integration, sandboxed code execution, and MCP (Model Context Protocol).

• ​Architecture & Memory Management: Implementing short-term and episodic memory (scratchpads, state graphs, vector-based retrieval, conversational buffer compaction).

• ​LLM Foundations: Advanced prompt engineering, chain-of-thought, ReAct (Reasoning + Acting), reflection loops, and output grounding/guardrails (e.g., NeMo Guardrails, Guardrails AI)
  

Preferred qualifications, capabilities, and skills:

• Experience building agent-based systems, orchestration patterns, or agent development tooling and evaluation frameworks
• Experience designing scalable inference or model serving architectures, including latency, throughput, and cost optimization
• Familiarity with responsible artificial intelligence practices, model risk concepts, and governance-by-design approaches
• Experience contributing to or maintaining widely used open-source software in machine learning or infrastructure ecosystems
• Domain knowledge applying machine learning to regulated financial services use cases