Back
Tier-1 brand, mid-level experience, metro location, and broad GenAI skillset increase candidate competition.
Core GenAI and MLOps skills transfer across industries, though enterprise advisory context adds moderate specificity.
Explicit 4–7 years plus many mandatory GenAI, MLOps, and cloud technologies makes shortlisting strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, develop, and deploy scalable Generative AI solutions using large language models (LLMs) and transformer architectures.
Build and optimize AI model pipelines using Python, PyTorch, Hugging Face Transformers, and orchestration frameworks like LangChain.
Collaborate with teams to productionize GenAI models on cloud platforms (Azure, AWS, GCP) ensuring scalability, compliance, and integration with enterprise applications.
4 to 7 years of work experience in relevant AI and software engineering roles.
Mandatory skills: Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, Azure/AWS/GCP cloud platforms, LangChain or similar orchestration frameworks, REST APIs with FastAPI or Flask, ML pipeline tools like MLflow or Weights & Biases, and CI/CD for ML.
Educational qualification: Bachelor’s degree in Technology (B.E/B.Tech) or Master’s degree (M.Tech/MCA).
Notice period: Not explicitly mentioned in the JD.
Experienced in customizing and fine-tuning foundation AI models using domain-specific datasets.
Skilled in implementing model deployment pipelines and integrating GenAI into enterprise environments with strong cloud platform expertise.
Operates effectively in an innovation-focused, emerging technology environment requiring hands-on development and collaboration with engineering and MLOps teams.