Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 brand, metro location, mid-level role with broad skills increases candidate competition.
Specialized GenAI skills required but ML engineering experience transfers across industries moderately well.
Explicit 4–7 years plus mandatory ML stack and cloud/MLops requirements make filters strict.
Design, develop, and deploy scalable Generative AI solutions using large language models (LLMs) and transformer architectures.
Build and optimize model pipelines and prompt engineering strategies using Python, PyTorch, Hugging Face Transformers, and orchestration frameworks like LangChain or Langgraph.
Collaborate with data engineers and MLOps teams to productionize GenAI models on cloud platforms such as Azure, AWS, or GCP, ensuring robustness, scalability, and compliance.
4 to 7 years of experience in relevant AI/ML roles.
Mandatory technical skills: Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, Azure/AWS/GCP cloud platforms, LangChain or similar orchestration frameworks, REST APIs (FastAPI/Flask), ML pipeline tools (MLflow, Weights & Biases), Git, and CI/CD for ML (e.g., Azure ML, SageMaker pipelines).
Educational requirement: Bachelor of Technology (B.E/B.Tech) or equivalent in a related field; M.Tech/MCA also acceptable.
Work Experience Required: 4 to 7 years. Notice period: Not explicitly mentioned in the JD.
Experienced in implementing generative AI solutions with hands-on expertise in both foundational model fine-tuning and production deployment on cloud platforms.
Familiar with orchestration frameworks for ML workflows and integrating GenAI capabilities into enterprise-scale systems.
Strong in building end-to-end ML pipelines including model evaluation, iteration, and API integration, with practical knowledge of ML lifecycle management and CI/CD for ML.