Tech Lead - Artificial Intelligence Engineer

Owns end-to-end delivery of how the organization builds LLM-powered applications: SDK/integration architecture, retrieval and agent design, guardrails, and evaluation. Makes the calls on which LLMs to use for which use cases and sets the observability/cost discipline around AI systems, but is measured on shipped outcomes — runs the offshore team day-to-day and stays hands-on to unblock delivery risk. Key Responsibilities Own delivery of LLM API integration and SDK patterns used across applications. Set organizational guidance on which LLM to use for what use case and drive delivery of multi-LLM scenarios. Define standards for advanced prompt engineering and context window management. Own delivery of RAG systems, including vector database selection/topology and knowledgebase design. Drive delivery of AI agent and multi-agent systems and tool-use/MCP integration patterns. Own guardrails delivery (safety, compliance, PII handling in prompts/outputs) — critical in a financial-services context. Define evaluation frameworks and real-time eval strategy; set standards for AI testing in CI/CD. Own latency profiling, AI observability, and cost tracking/management for LLM-backed systems. Run day-to-day delivery of the offshore development team: sprint commitments, code/design review, real-time unblocking, and hands‑on work on critical‑path AI features. Report delivery status, risks, and blockers to engineering leadership. Must-Have Qualifications 6+ years in software engineering, with 2+ years as a tech lead owning end-to-end delivery of LLM/AI-powered systems (not a pure design/review architect role). Proven track record of shipping AI-powered features on committed timelines, including hands‑on troubleshooting under delivery pressure. Strong, hands‑on Python skills at an architectural/systems level. Proven experience architecting LLM API integrations and SDK-level abstractions across multiple providers. Demonstrated judgment on model selection (cost, latency, capability trade‑offs) across use cases. Deep expertise in prompt engineering and context window management at scale. Proven design experience with RAG systems, including vector database architecture and knowledgebase design. Experience architecting AI agents/multi-agent systems and tool-use patterns (MCP or equivalent). Strong understanding of guardrails design — content safety, PII protection, compliance controls for AI outputs. Experience defining evaluation frameworks and integrating AI testing into CI/CD. Proven ability to design for latency, observability, and cost management of AI systems in production. Financial‑services or regulated‑industry experience strongly preferred given compliance/guardrail stakes. Strong stakeholder communication; able to directly manage day-to-day delivery of an offshore team (standups, unblocking, sprint accountability). Nice-to-Have Qualifications Direct experience with specific frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent). Experience with AWS Bedrock or comparable managed LLM platforms. Contributions to or deep familiarity with MCP (Model Context Protocol) implementations. Experience building internal LLM gateways. Familiarity with responsible‑AI/model‑risk‑management frameworks used in financial services. #J-18808-Ljbffr Photon