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Tier-1 firm, mid-level experience band, and Bangalore metro increase qualified applicant density.
Strong ML/LLM engineering skills transferable, but GenAI specialization adds moderate domain specificity.
Explicit 4–7 years plus many mandatory GenAI, ML, cloud, and MLOps technical requirements.
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Design, develop, and deploy scalable Generative AI solutions using large language models (LLMs) and transformer architectures.
Build and optimize model pipelines and integrate Generative AI capabilities into enterprise applications via APIs or interfaces on cloud platforms (Azure, AWS, or GCP).
Collaborate with data engineers and MLOps teams to productionize models ensuring robustness, scalability, and compliance.
4 to 7 years of work experience in relevant AI/ML roles.
Proficiency in Python, PyTorch, Hugging Face Transformers, and orchestration frameworks like LangChain or Langgraph.
Experience deploying AI solutions on cloud platforms (Azure, AWS, GCP) and working with ML pipeline tools (MLflow, Weights & Biases) plus CI/CD for ML (Azure ML, SageMaker pipelines).
Bachelor's degree in Engineering (B.E/B.Tech), M.Tech, or MCA.
Experienced in working with various large language models including OpenAI, Anthropic, and open-source models like LLaMA or Falcon.
Skilled at prompt engineering, fine-tuning foundation models, and implementing agentic AI solutions using relevant tools.
Comfortable collaborating across engineering and operational teams to ensure production readiness and compliance of AI deployments in an enterprise environment.