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Strong brand, metro location, and mid-level GenAI role increase applicant competition.
GenAI skills transfer across industries but require ML specialization, so medium sensitivity.
Explicit 4–7 years plus many mandatory ML/GenAI technical skills creates high filtering.
Design, develop, and deploy scalable Generative AI solutions leveraging large language models (LLMs) and transformer architectures.
Build and optimize model workflows using Python, PyTorch, Hugging Face, and orchestration frameworks like LangChain; integrate GenAI capabilities into enterprise systems.
Collaborate with data engineering and MLOps teams to productionize models on Azure, AWS, or GCP ensuring robustness, scalability, and compliance.
4 to 7 years of professional experience in relevant fields.
Bachelor's degree in Technology (B.E/B.Tech) or equivalent (M.Tech/MCA also acceptable).
Proficiency in Generative AI technologies including LLMs, Transformers, Python, PyTorch, Hugging Face Transformers.
Experience with cloud platforms (Azure, AWS, or GCP), orchestration frameworks (LangChain/Langgraph), REST APIs (FastAPI, Flask), ML pipeline tools (MLflow, Weights & Biases), and CI/CD for ML (e.g., Azure ML, SageMaker).
Experienced with fine-tuning foundation models and implementing agentic AI solutions using frameworks like LangChain or Crew.ai.
Able to evaluate model performance quantitatively and qualitatively, iterating to improve output accuracy and context relevance.
Hands-on experience integrating GenAI into enterprise applications and collaborating effectively with cross-functional teams in fast-paced emerging technology environments.