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Mid-level GenAI role with broad cloud tooling attracts moderate applicant competition.
Core ML/GenAI skills are transferable across industries but require specific GenAI experience.
Explicit 2–5 years plus many mandatory ML/GenAI, cloud, and MLOps skills enforce strict filters.
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Design, build, and deploy AI/ML models including Generative AI (LLMs), Retrieval-Augmented Generation pipelines, and Agentic AI workflows for automation and multi-step reasoning.
Develop scalable, production-ready AI/ML solutions leveraging Azure AI services or similar cloud platforms, optimizing for performance and cost efficiency.
Collaborate with cross-functional teams to integrate AI/ML innovations into real-world business applications while staying current with latest AI/ML research and tools.
2–5 years of professional experience in AI/ML development.
Education: B.E./B.Tech/M.Tech in Computer Science, AI/ML, Data Science, or related field.
Proficiency in Python and AI/ML frameworks including PyTorch or TensorFlow, LangChain, Transformers, and experience with LLMs, embeddings, and prompt engineering.
Hands-on experience with Azure AI services (preferred) or equivalent cloud AI platforms (AWS Sagemaker, GCP Vertex AI).
Experienced in end-to-end AI/ML project lifecycle from model training and fine-tuning to deployment using MLOps tools like MLflow, Docker, and Kubernetes.
Skilled in building advanced GenAI and RAG systems incorporating vector databases and Agentic AI frameworks suitable for scalable, production environments.
Comfortable working in cloud-based AI environments with cross-functional collaboration and focused on leveraging latest AI research for business problem solving.