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Tier-1 brand, mid-level ML role, hybrid Mumbai location and broad LLM/MLOps skillset driving high competition.
ML/LLM engineering skills transfer across industries, though consulting/advisory context rewards client-facing experience.
Explicit 4–8 year requirement plus extensive mandatory LLM, MLOps, cloud and infra skills increases shortlisting strictness.
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Lead AI/ML initiatives focusing on development and deployment of large language models (LLMs) and related technologies within a hybrid work environment.
Design, implement, and optimize AI/ML solutions including chatbots, recommendation systems, and translation services using Python, LLM frameworks, and big data technologies.
Manage end-to-end ML lifecycle covering model development, deployment, monitoring, and infrastructure using microservices, DevOps tools, and cloud platforms.
4-8 years of relevant experience in AI/ML or related fields.
Proficiency in Python, experience with LLM frameworks (Hugging Face Transformers, LangChain), big data processing (Spark), and software engineering skills including microservices and test-driven development.
Experience with DevOps, infrastructure as code (Terraform, CloudFormation), CI/CD pipelines, container orchestration (Kubernetes), and LLM deployment tools like vLLM and FastAPI.
Bachelor’s degree in Engineering (B.Tech), M.Tech, MCA, or MBA; Work Experience Required: 4-8 years.
Experienced AI/ML engineer with strong expertise in LLM development, optimization (quantization, knowledge distillation), and deployment in production environments.
Skilled in microservices architecture and MLOps best practices ensuring scalable, maintainable AI systems.
Familiar with cloud infrastructure (AWS, GCP, Azure) and a track record of building advanced AI solutions such as RAG systems, vector databases, and semantic search implementations.