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Mid-level GenAI role in metro with broad skillset and reputable employer, making applicant competition high.
Core GenAI and MLOps skills are broadly transferable across industries.
Explicit 6–8 years plus mandatory GenAI, LLM, and MLOps skills makes shortlisting highly strict.
Design, develop, and deploy AI and GenAI solutions including Retrieval-Augmented Generation (RAG) pipelines in production environments.
Fine-tune and evaluate Large Language Models (LLMs) and implement prompt engineering strategies and evaluation frameworks.
Collaborate cross-functionally to deliver AI-driven features while monitoring model performance, conducting experimentation, and contributing to AI system architecture.
6–8 years of professional experience in data science and AI projects.
Advanced proficiency in Python and experience with machine learning (supervised/unsupervised learning, NLP).
Hands-on experience with Generative AI including LLMs, prompt engineering, embeddings, and RAG architectures.
Experience with AI/ML tools such as LangChain/LlamaIndex, Scikit-learn, PyTorch/TensorFlow, HuggingFace/OpenAI APIs, vector databases, SQL, cloud platforms (AWS/Azure/GCP), and CI/CD for ML deployments.
Strong technical background in developing production-grade AI and GenAI solutions, specifically with RAG pipelines and LLM fine-tuning.
Experienced operating in complex, collaborative environments involving Engineering, Product, and SMEs to deliver scalable AI-powered features.
Comfortable managing ML lifecycle including experimentation, model monitoring, performance benchmarking, and contributing to AI architecture design.