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Tier-1 brand, Bangalore location, and mid-level 4–7 year range increase candidate competition.
GenAI and MLOps skills are transferable across industries but require specific ML experience.
Explicit 4–7 years plus mandatory GenAI, PyTorch, Hugging Face, cloud and MLOps skills.
Design, develop, and deploy scalable generative AI solutions leveraging LLMs and transformer architectures using Python, PyTorch, and Hugging Face.
Build and optimize AI model pipelines and workflows, including fine-tuning foundation models and prompt engineering for accurate outputs.
Collaborate with data engineers and MLOps teams to productionize and integrate GenAI capabilities into enterprise systems on cloud platforms (Azure, AWS, GCP).
4 to 7 years of relevant work experience in generative AI and related technologies.
Mandatory technical skills: Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, cloud platforms (Azure/AWS/GCP), orchestration frameworks (LangChain or similar), REST APIs development (FastAPI, Flask), ML pipeline tools (MLflow, Weights & Biases), and ML CI/CD platforms (Azure ML, SageMaker).
Educational qualification: Bachelor of Technology (B.E/B.Tech); M.Tech or MCA also acceptable.
Work Experience Required: 4 to 7 years; Notice Period: Not explicitly mentioned in the JD.
Experienced in deploying and scaling AI solutions in enterprise environments using cloud AI platforms like Azure AI Foundry or GCP Vertex.
Strong hands-on expertise in model fine-tuning, prompt engineering, and agentic AI implementation with orchestration frameworks such as LangChain.
Comfortable working in cross-functional teams with data engineers and MLOps to ensure robustness, scalability, and compliance of AI models in production.