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. Compensation, Benefits and Duration Minimum Compensation: USD 56,000 Maximum Compensation: USD 196,000 Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role. Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full-time employees. This position is not available for independent contractors No applications will be considered if received more than 120 days after the date of this post