Back
Strong employer brand, metro location, mid-level generalist GenAI title, and broad skillset increase applicant competition.
GenAI and LLM pipeline skills are transferable across industries but require specialized ML experience.
Explicit 3-5 years plus specific GenAI, PyTorch, cloud, LangChain and MLOps requirements enforce strict filtering.
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 LLMs and transformer architectures.
Fine-tune foundation models, optimize prompt engineering, and integrate GenAI capabilities into enterprise systems via APIs.
Collaborate with data engineers and MLOps teams to productionize models on cloud platforms (Azure/AWS/GCP) ensuring robustness and compliance.
3 to 5 years of relevant work experience.
Mandatory skills: Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, cloud platforms (Azure/AWS/GCP), LangChain or similar orchestration frameworks, REST APIs (FastAPI, Flask), ML pipeline tools (MLflow, Weights & Biases), Git, CI/CD for ML (Azure ML, SageMaker pipelines).
Education: B.E/B.Tech/M.Tech/MCA; MBA indicated but not clearly mandatory for technical role.
Notice period: Not explicitly mentioned in the JD.
Experienced in end-to-end GenAI solution lifecycle including model fine-tuning and deployment in cloud environments.
Strong hands-on expertise with orchestration frameworks and ML pipeline management for scalable AI applications.
Capable of collaborating cross-functionally with engineering and MLOps teams to ensure enterprise-grade AI product integration.