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Tier-1 brand, mid-level (4–7 yrs), Bangalore metro, and popular GenAI title drive high competition.
Specialized GenAI tooling increases domain bias, yet ML skills are reasonably transferable across industries.
Mandatory 4–7 years and numerous required GenAI/ML tools make shortlisting highly strict.
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Design and deploy scalable Generative AI solutions using large language models (LLMs) and transformer architectures.
Build and optimize AI model pipelines and integrate GenAI capabilities into enterprise applications on cloud platforms (Azure, AWS, GCP).
Collaborate with data engineering and MLOps teams to ensure robustness, scalability, and compliance of deployed AI models.
4 to 7 years of relevant work experience in Generative AI and ML engineering.
Degree requirement: Bachelor of Technology (B.E/B.Tech) or equivalent.
Strong skills required: Python, PyTorch, Hugging Face Transformers, cloud platforms (Azure/AWS/GCP), orchestration frameworks like LangChain, REST API frameworks (FastAPI or Flask), and ML pipeline tools (MLflow, Weights & Biases).
Experience with CI/CD for ML workflows using platforms like Azure ML or SageMaker Pipelines.
Experienced in working with foundation models including fine-tuning and prompt engineering for domain-specific applications.
Familiar with deploying AI solutions in production environments leveraging cloud AI platforms and MLOps practices.
Proficient in designing AI workflows using orchestration frameworks and integrating them through APIs for enterprise usage.