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Strong employer brand, metro Bangalore location, and a mid-level generalist GenAI role increase competition.
GenAI and cloud deployment skills transfer across industries but require ML specialization, so medium sensitivity.
Explicit 3–5 years requirement plus extensive mandatory ML/GenAI, cloud, and tooling skills raises filter strictness.
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Design, build, fine-tune, and deploy scalable Generative AI solutions leveraging large language models (LLMs) like OpenAI, Anthropic, Mistral, LLaMA, and Falcon.
Develop and implement AI model pipelines using Python, PyTorch, Hugging Face Transformers, LangChain, and integrate these solutions into enterprise systems using APIs and orchestration frameworks.
Collaborate with data engineers and MLOps teams to productionize AI models on cloud platforms (Azure, AWS, GCP) ensuring scalability, robustness, and compliance.
3 to 5 years of work experience in Generative AI and related technologies.
Proficiency in Python, PyTorch, Hugging Face Transformers, and cloud platforms Azure, AWS, or GCP.
Experience with orchestration frameworks like LangChain and API development using FastAPI or Flask.
Bachelor's or Master's degree in Engineering (B.E/B.Tech/M.Tech) or MCA; MBA degree mentioned but unclear if mandatory.
Strong hands-on expertise in deploying and fine-tuning foundation Generative AI models in enterprise environments with cloud-based production.
Experience with ML pipeline management and CI/CD practices for machine learning using tools like MLflow, Weights & Biases, Azure ML, or SageMaker pipelines.
Familiarity with advanced GenAI use cases such as agentic AI implementation and Retrieval-Augmented Generation (RAG) with vector databases and orchestration platform integration.