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Metro hiring for a visible ML lead role with broad GenAI requirements increases competition density.
Core ML engineering skills transfer across industries, but specialized LLM/agentic AI expertise increases domain specificity.
Extensive mandatory ML/GenAI stack and software-engineering practices required, raising screening rigidity.
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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 inference and deployment pipelines ensuring cost-effective production and engineering best practices.
Mentor junior engineers and collaborate with cross-functional teams in data science, research, and product to develop AI solutions responsibly and ethically.
Proficient in Python with strong fundamentals in NumPy, Pandas, scikit-learn.
Deep learning expertise using PyTorch or TensorFlow; hands-on experience with LLM frameworks like Hugging Face Transformers and LangChain.
Experience with Agentic AI frameworks (AutoGen, CrewAI, LangGraph) and RAG pipelines including semantic search and vector databases.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in architecting and productionizing GenAI solutions with strong software engineering skills including microservices, TDD, and concurrency.
Demonstrates ability to work on advanced AI topics such as agentic AI, LLM optimizations, and scalable inference pipelines.
Capability to mentor teams and drive ethical AI development within multidisciplinary technical environments.