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Niche GenAI skills reduce applicants but metro Tech Lead title still attracts moderate competition.
LLM and MLOps skills are broadly transferable across industries, so background sensitivity is low.
Explicit 8+ years plus deep GenAI, model optimization, and MLOps requirements enforce strict shortlisting.
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Build, deploy, and optimize scalable Large Language Model (LLM), Retrieval-Augmented Generation (RAG), and agent-based AI systems.
Architect and manage LLM inference and deployment pipelines ensuring efficient, cost-effective production at scale.
Collaborate cross-functionally with data science, research, and product teams; mentor junior engineers; uphold engineering best practices and ethical AI development.
8+ years of experience in data engineering or software engineering roles.
Bachelor's degree in Information Technology, Computer Science, or related field required.
Strong advanced Python skills with expertise in deep learning frameworks such as PyTorch or TensorFlow and hands-on experience with LLM frameworks like Hugging Face Transformers and LangChain.
Experience with Snowflake for data modeling, performance tuning, and SQL optimization.
Proven track record in architecting and deploying production-grade GenAI solutions including LLMs, RAG pipelines, and agentic AI frameworks.
Comfortable working in multidisciplinary teams interfacing with research, data science, and product groups and driving engineering excellence.
Experienced in managing end-to-end AI/ML lifecycle, with a mix of software engineering best practices and practical deployment expertise in cloud environments.