Overview

Role & responsibilities

Responsibilities

• Design and implement machine learning solutions using a mix of classical ML models and LLM-based approaches.
• Select appropriate techniques for each problem, balancing accuracy, performance, cost, and operational complexity.
• Build and train models for tasks such as classification, similarity matching, extraction, normalization, and ranking.
• Develop LLM workflows including embeddings, prompt design, and retrieval-augmented generation where applicable.
• Lead and mentor a team of backend and frontend developers, fostering a high-performing, collaborative engineering culture.
• Own the technical direction, architecture, and strategy for LLM, AI Agent, and ML initiatives.
• Architect and build AI Agents, RAG pipelines (including multimodal RAG), LLM-based services, and evaluation frameworks.
• Design, build, and deploy scalable AI/ML systems from concept to production.
• Collaborate closely with PMs, ML Engineers, and Data Scientists to convert business requirements into AI-driven solutions.
• Oversee full SDLC for your squadensuring strong engineering practices around coding, testing, quality, and reliability.
• Conduct detailed technical reviews and provide guidance to maintain code quality.
• Troubleshoot performance, scalability, and reliability issues in production AI systems.
• Champion continuous improvement, automation, and innovation within the team.
• Keep the team up-to-date with advances in LLMs, LLMOps, fine-tuning techniques, vector search, and generative AI.