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Mid-level GenAI role, metro location, and broad skillset create high applicant density.
Specialized GenAI/NLP expertise increases domain bias, though core AI skills remain transferable.
Explicit 4–7 years and mandatory GenAI, LLM, cloud, and MLOps skills drive high shortlisting strictness.
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Design, develop, fine-tune, and deploy Generative AI/LLM and NLP solutions across enterprise cloud platforms with a focus on scalable, secure, production-ready AI-powered systems.
Lead AI model implementation, governance, and integration within CI/CD pipelines in collaboration with cross-functional teams (product, data, engineering).
Mentor junior AI engineers and drive continuous improvement in AI delivery, automation, and operational efficiency while embedding governance, risk, and compliance in AI initiatives.
4 to 7 years of experience in AI/ML/Data Science with at least 3 years specifically in Generative AI or NLP.
Proficiency in Python for AI development, hands-on experience with LLMs (e.g., OpenAI, AWS Bedrock), Generation-augmented Retrieval pipelines, vector databases (e.g., Faiss, Pinecone), and deployment on cloud platforms like AWS, Azure, or GCP.
Experience with AI governance, data privacy, and security considerations in production environments.
Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related field.
Demonstrated success in delivering AI/ML solutions in regulated or compliance-sensitive environments with cross-functional collaboration.
Ability to manage end-to-end AI model lifecycle including training, fine-tuning, deployment, monitoring, and retraining in cloud CI/CD environments.
Experienced in mentoring engineers and advancing best practices in MLOps, responsible AI, and model governance within enterprise-scale AI projects.