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Mid-level popular AI role, metro location, and broad GenAI skill requirements drive high competition.
Core GenAI engineering skills transfer across industries but regulatory and healthcare requirements increase domain sensitivity.
Mandatory 6+ years, specific GenAI stack, and regulatory compliance make shortlisting highly strict.
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Lead design, build, and operationalize scalable enterprise-grade Generative AI and Agentic AI solutions with end-to-end ownership from problem framing to production deployment and optimization.
Establish engineering standards for AI development including experimentation, evaluation, secure deployment, and operational monitoring to ensure responsible, high-quality AI solutions.
Collaborate across global teams and contribute beyond model development including data engineering, backend services, application logic, and UI components to deliver reliable production AI systems.
Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
6+ years of relevant experience in Data Science, Machine Learning, or AI engineering.
Hands-on experience building and deploying Generative AI or Agentic AI solutions in enterprise environments from POC to production.
Proficiency in Python, ML frameworks like PyTorch or TensorFlow, AI orchestration frameworks such as Langchain, and experience with Databricks, AWS, and CI/CD automation via GitHub Actions.
Experienced in end-to-end AI solution delivery including data pipelines, backend services, and user-facing components enabling scalable production systems.
Strong expertise in NLP, transformers, embeddings, vector databases, RAG architectures, and managing AI solution performance, observability, and governance in regulated environments.
Comfortable working in globally distributed teams, collaborating cross-functionally, and leading technical standards and mentorship in AI engineering practices.