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Mid-level role in metros with a known brand but specialized GenAI skills limit broad applicant density.
Role requires specialized LLM, RAG, and fine-tuning experience, so backgrounds must be ML/AI-focused.
Explicit 6–8 years and many mandatory ML/GenAI tech requirements make filtering stringent.
Design, develop, deploy, and optimize machine learning and GenAI solutions including RAG pipelines and LLM fine-tuning in production environments.
Develop prompt engineering strategies and evaluation frameworks to improve AI-driven features in collaboration with Engineering, Product, and SMEs.
Monitor model performance, conduct experimentation such as A/B testing, and contribute to AI architecture design and scalable ML pipeline implementation.
6–8 years of relevant work experience in Data Science or AI.
Advanced proficiency in Python programming and experience with machine learning, generative AI (LLMs, prompt engineering), and RAG architectures.
Familiarity with ML tools and frameworks such as LangChain, LlamaIndex, Scikit-learn, PyTorch, TensorFlow, HuggingFace, OpenAI APIs, and vector databases like Pinecone or FAISS.
Experience with SQL, cloud platforms (AWS/Azure/GCP), CI/CD for ML deployments, and model tracking tools (e.g., MLflow).
Experienced in deploying and fine-tuning Large Language Models and implementing Retrieval-Augmented Generation pipelines within production systems.
Capable of collaborating with cross-functional teams (Engineering, Product, SMEs) to deliver AI-driven business solutions.
Skilled in ML experimentation, model monitoring, and architecture design for scalable AI-powered systems.