Technical Lead AI Initiatives

About your role:\nWe are seeking a highly skilled Technical Lead for AI Development to drive the architecture, design, and execution of advanced AI systems using LLM frameworks, multi-agent architectures, RAG pipelines, and Model Context Protocol (MCP) integrations. The ideal candidate has strong hands-on experience building production-grade AI features, orchestrating agent ecosystems, evaluating model performance, and iterating through continual refinements.\nYou will lead a team of engineers, collaborate with product and research teams, and play a key role in shaping our AI strategy and platform capabilities.\nKey Responsibilities:\nAI Architecture & Development\nDesign and implement multi-agent systems, including agent orchestration, delegation, and tool interaction patterns.\nBuild scalable RAG (Retrieval-Augmented Generation) architectures using vector databases, embedding pipelines, and data chunking strategies.\nIntegrate and extend MCP (Model Context Protocol) tools for robust model-tool communication and workflow automation.\nLead development of AI-based features, prototypes, and production solutions using LLM APIs or self-hosted models.\nArchitect and optimize prompt engineering, prompt chains, agent loops, and refinement pipelines.\nModel Evaluation & Continuous Improvement\nImplement and maintain agent evaluation frameworks (agent evals, scenario tests, regression testing).\nDesign automated evaluation harnesses for LLM quality, reliability, hallucination control, and performance metrics.\nDrive iterative improvements through A/B testing, reward models, and feedback loops.\nMonitor system performance, latency, cost, and reliability — and implement optimization strategies.\nTechnical Leadership\nLead and mentor engineers working on AI, data, and backend components.\nCollaborate with product managers, researchers, and cross-functional teams to align tech strategy with business outcomes.\nConduct code reviews, enforce best practices, and maintain architectural standards.\nOwn technical roadmaps, sprint planning, and engineering execution.\nSystems & Infrastructure\nWork with cloud platforms (AWS/GCP/Azure) to deploy scalable AI services.\nIntegrate vector databases (Pinecone, Weaviate, Elasticsearch, etc.).\nBuild APIs and microservices to expose AI capabilities to internal and external stakeholders.\nMaintain secure, compliant, and efficient data pipelines for ingestion and retrieval.\nQualifications\nBachelor’s/Master’s degree in Computer Science, Engineering, AI, or related field.\n8+ years of software engineering experience with strong backend architecture skills.\n3+ years deep experience with LLMs, GPT models, agents, or advanced ML systems.\nStrong hands-on experience with:\nMCP tools and LLM tool integration\nAgent frameworks (e.g., OpenAI Agents, LangChain, LlamaIndex, custom agents)\nRAG pipelines, embedding models, vector stores\nAgent evaluation, reliability testing, and model refinements\nProficiency in Python, TypeScript/Node.js, or similar languages.\nExperience deploying LLM apps and APIs in production environments.\nDeep understanding of AI limitations, hallucination control, and safety measures.\nPreferred / Nice to Have\nExperience with:\nFine-tuning LLMs\nOpenAI API, Claude, or Azure OpenAI\nDistributed embeddings and high-throughput retrieval systems\nMLOps frameworks\nKnowledge of DevOps, CI/CD, containerization (Docker/Kubernetes).\nPrior leadership experience managing small to mid-size engineering teams.\n\nThank you!