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Strong brand, mid-level 5+ years, popular data/ML title, and metro hiring amplify applicant competition.
Specialized ML/LLM skills are transferable across industries but require strong applied ML background.
Explicit 5+ years, Master’s/PhD required and many mandatory ML/LLM and MLOps skills increase strictness.
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Design and develop custom ML, Gen AI, NLP, and LLM models for batch and streaming AI/ML pipelines with components like data ingestion, preprocessing, search and retrieval, RAG, fine-tuning, and prompt engineering.
Collaborate with product teams, data science, MLOps, and engineering to integrate ML models into production systems, ensuring alignment with technical and business requirements.
Maintain comprehensive documentation of ML processes and troubleshoot complex issues in model development and data pipelines adhering to specified governance and best practices.
Master’s or Ph.D. degree in Computer Science, Mathematics, Statistics, Computational Linguistics, Engineering, or related field.
5+ years of professional experience in developing ML, NLP, RAG, and Gen AI solutions using large structured and unstructured data sets.
Proficiency in Python coding for production and hands-on experience with LLMs, generative AI frameworks (LangGraph, LangChain), PyTorch, TensorFlow, AWS, Git workflows, Docker, and Kubernetes.
Experience with feature engineering, A/B testing, experimental design, fine-tuning LLMs, and constructing retrieval-augmented generation (RAG) pipelines.
Experienced ML/NLP/Gen AI scientist familiar with end-to-end development and deployment of advanced AI models in production environments.
Skilled in collaborative cross-functional work involving product teams, MLOps, and software engineers to deliver operational AI solutions.
Able to implement and innovate on complex AI techniques like prompt engineering, agentic AI, and MCP concepts within a structured governance and best practices framework.