Posted May 7, 2026
Key Responsibilities:
Design, build, and deploy generative AI solutions using LLMs such as OpenAI, Anthropic, Mistral, or open-source models (e.g., LLaMA, Falcon). - Fine-tune and customize foundation models using domain-specific datasets and techniques. - Develop and optimize prompt engineering strategies to drive accurate and context-aware model responses. - Implement model pipelines using Python and ML frameworks such as PyTorch, Hugging Face Transformers, or LangChain. - Collaborate with data engineers and MLOps teams to productionize GenAI models on cloud platforms (Azure/AWS/GCP). - Ensure robustness, scalability, and compliance of AI models in deployment environments. - Integrate GenAI into enterprise applications via APIs or custom interfaces. - Evaluate model performance using quantitative and qualitative metrics, and improve outputs through iterative experimentation. - Stay up to date with the latest research in GenAI, foundation models, and relevant open-source tools. Qualifications Required:
3 to 5 years of experience in Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, and Azure/AWS/GCP cloud platforms. - Proficiency in LangChain/Langgraph or similar orchestration frameworks, REST APIs, FastAPI, Flask, ML pipeline tools (MLflow, Weights & Biases), Git, and CI/CD for ML. - Preferred skills include knowledge in RAG (Retrieval-Augmented Generation), Vector DBs (FAISS, Pinecone, Weaviate), Streamlit/Gradio for prototyping, Docker, Kubernetes for model deployment, data preprocessing & feature engineering, and NLP libraries such as spaCy, NLTK, Transformers. - Education qualification: B.E/B.Tech/M.Tech/MCA with a preference for Master of Business Administration or Bachelor of Technology degrees. Key Responsibilities:
Design, build, and deploy generative AI solutions using LLMs such as OpenAI, Anthropic, Mistral, or open-source models (e.g., LLaMA, Falcon). - Fine-tune and customize foundation models using domain-specific datasets and techniques. - Develop and optimize prompt engineering strategies to drive accurate and context-aware model responses. - Implement model pipelines using Python and ML frameworks such as PyTorch, Hugging Face Transformers, or LangChain. - Collaborate with data engineers and MLOps teams to productionize GenAI models on cloud platforms (Azure/AWS/GCP). - Ensure robustness, scalability, and compliance of AI models in deployment environments. - Integrate GenAI into enterprise applications via APIs or custom interfaces. - Evaluate model performance using quantitative and qualitative metrics, and improve outputs through iterative experimentation. - Stay up to date with the latest research in GenAI, foundation models, and relevant open-source tools. Qualifications Required:
3 to 5 years of experience in Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers, and Azure/AWS/GCP cloud platforms. - Proficiency in LangChain/Langgraph or similar orchestration frameworks, REST APIs, FastAPI, Flask, ML pipeline tools (MLflow, Weights & Biases), Git, and CI/CD for ML. - Preferred skills include knowledge in RAG (Retrieval-Augmented Generation), Vector DBs (FAISS, Pinecone, Weaviate), Streamlit/Gradio for prototyping, Docker, Kubernetes for model
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