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Tier-2 brand, mid-level generalist ML title, and metro location increase applicant competition.
Strong ML/AI technical emphasis but industry domain transferable; moderate specificity to legal workflows.
Explicit 4–5 years requirement plus mandatory production ML/GenAI stack and tooling increases filter strictness.
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Design, train, and optimize custom ML/DL algorithms and deep neural networks to solve complex business predictive problems.
Build and orchestrate production-level autonomous GenAI agents and multi-agent systems using Python frameworks.
Architect and manage advanced Retrieval-Augmented Generation (RAG) pipelines including semantic chunking, multi-stage retrieval, and vector store integration.
3 to 4 years of professional experience as a Data Scientist, ML Engineer, or AI Developer in production software.
Proven expertise in Python with libraries such as scikit-learn, PyTorch, or TensorFlow.
Hands-on experience with generative AI technologies including large language models, vector databases (e.g., Pinecone, Qdrant), and prompt tuning.
Proficiency in SQL and data processing using pandas or NumPy.
Experienced in deploying machine learning models from experimentation to scalable, robust production environments.
Skilled in developing complex GenAI workflows and autonomous AI agents with emphasis on advanced Python engineering practices.
Comfortable working with cloud infrastructure for AI/ML workloads, preferably with AWS platforms like SageMaker or Bedrock (good to have).