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Popular mid-level ML/GenAI role in a metro office at a well-known firm increases candidate competition.
Core ML/LLM, RAG, and MLOps skills are highly transferable across industries.
Explicit 6–8 years and extensive mandatory ML/GenAI, LLM, and infrastructure skills enforce strict shortlisting.
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Lead design, development, and deployment of machine learning and generative AI (GenAI) solutions including Retrieval-Augmented Generation (RAG) pipelines and prompt engineering.
Fine-tune, evaluate, and monitor Large Language Models (LLMs) in production, ensuring model performance, drift detection, and reliability.
Collaborate cross-functionally with Engineering, Product, and Subject Matter Experts to deliver AI-powered features and contribute to AI system architecture design.
6-8 years of professional experience in data science or related field.
Advanced proficiency in Python and expertise in machine learning including supervised/unsupervised learning and NLP.
Experience with GenAI technologies such as LLMs, prompt engineering, RAG architectures, and vector search.
Hands-on experience with ML frameworks (PyTorch/TensorFlow), ML tools (LangChain/LlamaIndex), vector databases, cloud platforms (AWS/Azure/GCP), and CI/CD for ML deployments.
Experienced in deploying and scaling AI/ML applications with strong operational responsibility for model monitoring and MLOps workflows.
Skilled in advanced GenAI techniques including prompt engineering, fine-tuning using LoRA/PEFT/QLoRA, and multi-agent frameworks.
Comfortable working in cross-disciplinary teams involving engineering, product, and domain experts to develop AI-driven business solutions.