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Tier-1 brand, metro location, and mid-level experience raise candidate competition.
Specialized ML and LLM technical requirements are transferable but still require domain-specific expertise.
Explicit 4–7 years plus mandatory LLM, PyTorch, cloud and ML pipeline skills make filtering strict.
Design, develop, and deploy scalable Generative AI solutions using LLMs (OpenAI, Anthropic, Mistral, LLaMA, Falcon) and transformer architectures.
Implement and optimize model pipelines with Python, PyTorch, Hugging Face Transformers, LangChain, and orchestrate deployments on Azure, AWS, or GCP cloud platforms.
Integrate GenAI into enterprise applications via APIs, ensure model robustness, scalability, compliance, and collaborate with data engineering and MLOps teams for productionization.
4 to 7 years of relevant work experience in Generative AI or related fields.
Proficiency in Python, PyTorch, Hugging Face Transformers, and experience with cloud AI platforms (Azure, AWS, GCP).
Experience with orchestration frameworks (LangChain, Langgraph), REST API development (FastAPI, Flask), ML pipeline tools (MLflow, Weights & Biases), and CI/CD for ML pipelines (Azure ML, SageMaker).
Bachelor's degree in Engineering or Technology (B.E./B.Tech) or Master's (M.Tech/MCA).
Strong hands-on experience with foundation model fine-tuning, prompt engineering, and agentic AI implementation techniques.
Demonstrated ability to productionize AI models on cloud platforms collaborating effectively with engineering and MLOps teams.
Experience working with emerging technologies in AI, comfortable integrating complex ML workflows and APIs in enterprise environments.