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Tier-1 employer, mid-level metro GenAI role attracts many qualified applicants.
Specialized GenAI skills are transferable across industries but require specific LLM and MLOps experience.
Explicit 4–7 years and mandatory GenAI, PyTorch, cloud, and ML pipelines skills enforce strict filters.
Design, develop, fine-tune, and deploy scalable generative AI solutions with large language models (LLMs) using Python, PyTorch, and Hugging Face Transformers.
Implement orchestration frameworks, ML pipelines, and APIs to integrate GenAI capabilities into enterprise applications across cloud platforms (Azure, AWS, GCP).
Ensure robustness, scalability, compliance, and continuous improvement of AI models in production with collaboration across teams and use of CI/CD tools.
4 to 7 years of relevant work experience in generative AI and ML engineering.
Bachelor's degree in Technology (B.E./B.Tech) or related fields (M.Tech/MCA preferred).
Proficiency in Python, PyTorch, Hugging Face Transformers, cloud platforms (Azure/AWS/GCP), LangChain or similar orchestration frameworks, REST APIs with FastAPI or Flask, and ML pipeline tools such as MLflow or Weights & Biases.
Experience with Git and CI/CD practices for ML (e.g., Azure ML or SageMaker Pipelines).
Experienced in productionizing GenAI models using cloud AI platforms and orchestration tools with strong technical ownership over end-to-end model deployment.
Skilled in prompt engineering, agentic AI implementation, and evaluating model performance with iterative experimentation to improve outputs.
Comfortable working in emerging technology domains, integrating advanced AI capabilities into enterprise environments, and collaborating with multi-disciplinary teams including data engineers and MLOps.