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Strong employer brand, mid-level generalist ML role, metro location, and broad GenAI requirements increase competition.
Specialized GenAI/LLM engineering skills limit transferability across non-ML industries.
Explicit 4–7 years and mandatory ML/LLM, PyTorch, cloud, LangChain, and CI/CD requirements enforce a high filter.
Design, develop, and deploy scalable Generative AI solutions using large language models (LLMs) and transformer architectures.
Build and optimize ML pipelines, orchestrate model workflows, and integrate GenAI capabilities into enterprise applications on cloud platforms (Azure/AWS/GCP).
Collaborate with data engineers and MLOps teams to ensure robustness, scalability, and compliance of AI models in deployment environments.
4 to 7 years of relevant work experience with Generative AI technologies.
Proficiency in Python, PyTorch, Hugging Face Transformers, and orchestration frameworks like LangChain.
Experience deploying AI/ML solutions on cloud platforms such as Azure, AWS, or GCP including CI/CD practices (Azure ML or SageMaker Pipelines).
Bachelor of Technology (B.E/B.Tech) degree required.
Strong hands-on experience with large language models (OpenAI, Anthropic, Mistral, LLaMA, Falcon) and fine-tuning foundation models.
Proficient in building APIs (FastAPI, Flask) and managing ML pipelines with tools like MLflow or Weights & Biases.
Familiarity with integrating GenAI solutions into enterprise-scale applications and working in cloud-native MLOps environments.