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Mid-level ML role in a metro with known brand and common skillset, so highly competitive.
Core ML/AI skills transfer across industries, though Generative AI specifics moderately narrow fit.
Explicit years plus mandatory ML/GenAI tools and production engineering practices make filters strict.
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Design, train, and optimize custom ML/DL algorithms including Transformers, CNNs, and RNNs for business predictive problems.
Build and deploy production-level generative AI workflows such as Retrieval-Augmented Generation (RAG) pipelines and autonomous AI Agents using Python frameworks.
Write production-grade, modular, and performance-optimized Python code adhering to engineering best practices and assist in cloud migration for AI/ML workloads.
3 to 4 years of professional experience as a Data Scientist, ML Engineer, or AI Developer in a production software environment.
Proficient in Python and core ML/DL libraries (scikit-learn, PyTorch, TensorFlow).
Experience with generative AI tools including LLMs, vector databases (e.g., Pinecone, Qdrant, Milvus, Chroma), and prompt graph tuning.
Strong data engineering skills including optimized SQL, data cleaning, and pipeline construction using pandas or NumPy.
Experienced in end-to-end AI productization, transitioning models from research notebooks to scalable production environments.
Skilled in architecting high-throughput, multi-stage generative AI pipelines and autonomous agent structures.
Capable of blending deep ML expertise with software engineering rigor and cloud infrastructure alignment, preferably with AWS experience.