AI Engineering Lead

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com Job Description We're looking for an experienced and visionary AI Engineering Lead to join our growing team in Hyderabad, India. In this role, you'll lead the design, development, and deployment of cutting-edge AI solutions that drive real business impact. You'll combine technical excellence with strategic thinking, mentoring talented engineers while collaborating closely with stakeholders to translate complex AI challenges into scalable, production-ready systems. This is an opportunity to shape the future of AI engineering within our organization while fostering a culture of innovation, transparency, and continuous learning. Lead end-to-end AI project delivery with clear governance frameworks, ensuring transparent communication of risks, tradeoffs, and technical decisions to clients and internal stakeholders Design and architect robust AI systems, including RAG (Retrieval-Augmented Generation) systems, agentic frameworks, and LLM-powered solutions optimized for production environments Conduct feasibility assessments to determine the optimal technical approach—whether prompting, RAG, fine-tuning, classical machine learning, or hybrid solutions—grounded in evidence and business requirements Develop and implement advanced prompt engineering techniques, including instruction design, few-shot learning, structured outputs, and tool/agent orchestration Design comprehensive evaluation frameworks incorporating LLM-as-a-judge methodologies, custom metrics (recall@k, precision@k), and structured go/no-go decision gates Execute rigorous, data-driven experiments across prompts, retrievers, chunking strategies, and models, documenting findings and iterating based on evidence rather than intuition Identify, categorize, and mitigate model failure modes including hallucinations, retrieval gaps, and instruction-following errors Build and maintain scalable inference infrastructure, CI/CD pipelines, and deployment automation for AI and machine learning models Design and implement MLOps/LLMOps automation across the full lifecycle: experiment tracking, model versioning, deployment, monitoring, retraining, and observability Architect APIs, microservices, and orchestration layers optimized for latency, cost, reliability, and security Mentor junior engineers, fostering technical growth and a collaborative problem-solving culture Contribute to business development initiatives, including proposal writing and feasibility studies for new AI opportunities Define ethical boundaries and guardrails for AI systems, ensuring responsible and transparent deployment Qualifications **Required Skills and Experience:** 6+ years of hands-on experience building, deploying, and maintaining AI solutions in production environments Expert-level proficiency in Python with strong software engineering practices (Git, code review, testing) Proven expertise in designing and implementing RAG systems, including chunking strategies, embedding models, retrieval optimization, reranking, and evaluation methodologies Solid experience with cloud platforms (AWS, Azure, or GCP) including containerization, orchestration, and infrastructure management Demonstrated track record with MLOps/LLMOps tools and frameworks (MLflow, Weights & Biases, or equivalent) Strong hands-on experience with LLM