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Strong brand, mid-level ML role, metro location, and broad skillset increase candidate competition.
Advanced MLOps and compliance expectations limit transferability across industries, though core ML skills remain reusable.
Explicit 3-5 years plus many mandatory MLOps, GenAI, cloud and security skills enforce strict filters.
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Design and engineer end-to-end machine learning pipelines and platforms supporting classical ML, deep learning, and large language models with secure, scalable infrastructure.
Lead platform strategy and standards development, collaborating with DevOps, Security, Compliance, and Product teams to deliver enterprise-grade AI developer experience.
Own performance optimization, observability, security controls, and reusable platform components to enable hundreds of ML practitioners to prototype, deploy, and monitor models effectively.
3-5 years of experience in AI/ML and enterprise software development.
Strong expertise in machine learning algorithms including regression, tree-based models, clustering, time-series, deep learning (CNNs, RNNs, transformers) and large language models with operationalization experience.
Proficiency in Python and Java, containerization (Docker, Kubernetes), cloud platforms (AWS, Azure or GCP), and MLOps tools such as Kubeflow, SageMaker Pipelines, GitHub Actions.
Experience with GenAI tooling including vector databases, RAG pipelines, prompt engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel).
Senior individual contributor with deep technical expertise in building scalable, secure, end-to-end ML/GenAI platforms rather than basic model prototyping.
Experienced in integrating multitechnology stacks including AI SaaS/PaaS and custom ML microservices in enterprise environments.
Capable of strategic platform leadership including technical standards, KPI ownership, and cross-functional collaboration with Security, Compliance, and Product stakeholders.