Technical Lead - AI Development

Job Description:About your role:

We 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.

You 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.

Key Responsibilities:AI Architecture & Development:

• Design and implement multi-agent systems, including agent orchestration, delegation, and tool interaction patterns.

• Build scalable RAG (Retrieval-Augmented Generation) architectures using vector databases, embedding pipelines, and data chunking strategies.

• Integrate and extend MCP (Model Context Protocol) tools for robust model-tool communication and workflow automation.

• Lead 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.

Model Evaluation & Continuous Improvement:

• Implement and maintain agent evaluation frameworks (agent evals, scenario tests, 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.

Technical Leadership:

• 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.

Systems & Infrastructure:

• Work with cloud platforms (AWS/GCP/Azure) to deploy scalable AI services.

• Integrate vector databases (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.