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.