Technical Lead AI Initiatives

As a highly skilled Technical Lead for AI Development, your role will involve driving the architecture, design, and execution of advanced AI systems using LLM frameworks, multi-agent architectures, RAG pipelines, and Model Context Protocol (MCP) integrations. You will be responsible for leading a team of engineers, collaborating with product and research teams, and playing a key role in shaping the AI strategy and platform capabilities. Key Responsibilities: - Design and implement multi-agent systems, including agent orchestration, delegation, and tool interaction patterns. - Build scalable RAG architectures using vector databases, embedding pipelines, and data chunking strategies. - Integrate and extend MCP tools for robust model-tool communication and workflow automation. - Lead the development of AI-based features, prototypes, and production solutions using LLM APIs or self-hosted models. - Architect and optimize prompt engineering, prompt chains, agent loops, and refinement pipelines. - Implement and maintain agent evaluation frameworks, scenario tests, and regression testing. - Design automated evaluation harnesses for LLM quality, reliability, hallucination control, and performance metrics. - Drive iterative improvements through A/B testing, reward models, and feedback loops. - Monitor system performance, latency, cost, and reliability, and implement optimization strategies. - Lead and mentor engineers working on AI, data, and backend components. - Collaborate with product managers, researchers, and cross-functional teams to align tech strategy with business outcomes. - Conduct code reviews, enforce best practices, and maintain architectural standards. - Own technical roadmaps, sprint planning, and engineering execution. - Work with cloud platforms (AWS/GCP/Azure) to deploy scalable AI services. - Integrate vector databases such as Pinecone, Weaviate, Elasticsearch, etc. - Build APIs and microservices to expose AI capabilities to internal and external stakeholders. - Maintain secure, compliant, and efficient data pipelines for ingestion and retrieval. Qualifications: - Bachelors/Masters degree in Computer Science, Engineering, AI, or related field. - 8+ years of software engineering experience with strong backend architecture skills. - 3+ years deep experience with LLMs, GPT models, agents, or advanced ML systems. - Strong hands-on experience with MCP tools, LLM tool integration, agent frameworks, RAG pipelines, embedding models, agent evaluation, reliability testing, and model refinements. - Proficiency in Python, TypeScript/Node.js, or similar languages. - Experience deploying LLM apps and APIs in production environments. - Deep understanding of AI limitations, hallucination control, and safety measures. Preferred / Nice to Have: - Experience with fine-tuning LLMs, OpenAI API, Claude, or Azure OpenAI. - Knowledge of distributed embeddings, high-throughput retrieval systems, MLOps frameworks, DevOps, CI/CD, and containerization (Docker/Kubernetes). - Prior leadership experience managing small to mid-size engineering teams. Thank you! As a highly skilled Technical Lead for AI Development, your role will involve driving the architecture, design, and execution of advanced AI systems using LLM frameworks, multi-agent architectures, RAG pipelines, and Model Context Protocol (MCP) integrations. You will be responsible for leading a team of engineers, collaborating with product and research teams, and playing a key role in shaping the AI strategy and platform capabilities. Key Responsibilities: - Design and implement multi-agent systems, including agent orchestration, delegation, and tool interaction patterns. - Build scalable RAG architectures using vector databases, embedding pipelines, and data chunking strategies. - Integrate and extend MCP tools for robust model-tool communication and workflow automation. - Lead the development of AI-based features, prototypes, and production solutions using LLM APIs or self-hosted models. - Architect and optimize prompt engineering, prompt chains, agent loops, and refinement pipelines. - Implement and maintain agent evaluation frameworks, scenario tests, and regression testing. - Design automated evaluation harnesses for LLM quality, reliability, hallucination control, and performance metrics. - Drive iterative improvements through A/B testing, reward models, and feedback loops. - Monitor system performance, latency, cost, and reliability, and implement optimization strategies. - Lead and mentor engineers working on AI, data, and backend components. - Collaborate with product managers, researchers, and cross-functional teams to align tech strategy with business outcomes. - Conduct code reviews, enforce best practices, and maintain architectural standards. - Own technical roadmaps, sprint planning, and engineering execution. - Work with cloud platforms (AWS/GCP/Azure) to deploy scalable AI services. - Integrate vector databases such as Pinecone, Weaviate,