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Metro mid-level LLM/MLOps role with niche skills yields moderate competition.
Highly specialized AI/LLM and MLOps expertise limits cross-industry transferability.
Explicit 4–6 years plus many mandatory LLM, MLOps, and framework requirements create high filtering.
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Design, develop, fine-tune, and deploy Generative AI applications including large language models (LLMs).
Manage AI workflows with agentic frameworks (LangChain, AutoGen, CrewAI) and develop machine learning pipelines using Apache Airflow, MLflow, and DBT.
Ensure scalable AI model deployment on cloud platforms (AWS, Azure, GCP) using MLOps/LLMOps best practices and lead integration with cross-functional teams.
4-6 years total work experience in AI/ML product development.
Strong proficiency in Python, NLP, TensorFlow, PyTorch, and experience with LLM fine-tuning and agentic frameworks like LangChain, AutoGen, and CrewAI.
Experience with cloud platforms (AWS, Azure, or Google Cloud), containerization tools (Docker, Kubernetes), and CI/CD pipelines for MLOps implementations.
Proven capability in developing scalable AI/ML workflows and deploying models including multimodal LLMs and NER applications; R&D mindset required.
Experienced in hands-on AI model fine-tuning, deployment, and managing end-to-end MLOps pipelines in cloud environments.
Comfortable driving innovation with an R&D-focused approach and adopting latest AI advancements to production projects.
Capable of leading cross-functional collaborations and possess a technical leadership orientation in the AI/ML domain.