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Mid-level ML platform role in a metro with broad GenAI/MLOps requirements increases candidate competition.
Requires deep ML, MLOps and GenAI expertise, limiting transferability from non-AI backgrounds.
Explicit 3–5 year requirement plus extensive GenAI, cloud, and MLOps tech stack enforces strict filters.
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Design and own ML and generative-AI platform infrastructure enabling secure, scalable end-to-end ML workflows for hundreds of practitioners.
Develop, deploy, and optimize production-grade ML microservices and full-stack AI applications with performance, cost, and security considerations.
Define platform strategy, technical standards, and embed governance partnering cross-functionally with DevOps, Security, Compliance, and Product teams.
3-5 years of experience in AI/ML and enterprise software development.
Expertise in ML algorithms including classical methods, deep learning, and modern LLM/RAG techniques.
Proficiency with Python and Java, containerization (Docker/Kubernetes), cloud platforms (AWS/Azure/GCP), and MLOps tools such as Kubeflow or SageMaker Pipelines.
Experience with GenAI tooling including vector databases, prompt-engineering DSLs, and agent frameworks like LangChain or Semantic Kernel.
Senior individual contributor comfortable both hands-on engineering and strategic platform design in a complex enterprise environment.
Experienced in operationalizing scalable AI SaaS/PaaS platforms and integrating custom ML services with strong performance tuning and cost optimization skills.
Strong communicator capable of translating technical concepts to business stakeholders and aligning AI infrastructure with enterprise governance and compliance requirements.