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Mid-level ML role in a metro with a common title and broad MLOps/GenAI requirements increases competition.
Core ML, MLOps and GenAI skills transfer across industries, so background fit sensitivity is low.
Explicit 3–5 years plus many mandatory MLOps, GenAI, cloud and containerization skills raises strictness.
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Design and build end-to-end machine learning platforms and pipelines, including data ingestion, model training, deployment and monitoring, using tools like Kubeflow and SageMaker Pipelines.
Develop production-grade microservices for ML models with containerization and API exposure, ensuring secure and scalable integration with downstream applications.
Establish platform strategy, technical standards and governance by collaborating with DevOps, Security, and Compliance teams to deliver an enterprise-grade AI developer experience.
3-5 years of experience in AI/ML and enterprise software development.
Expertise in machine learning algorithms including regression, ensembles, deep learning (CNN, RNN, transformers) and LLM/RAG techniques.
Proficiency in Python and Java, containerization technologies (Docker, Kubernetes), cloud platforms (AWS, Azure, GCP) and MLOps tools (GitHub Actions, SageMaker Pipelines).
Experience with GenAI tooling such as vector databases, RAG pipelines, and agent frameworks (e.g., LangChain, Semantic Kernel).
Technical leader at the intersection of engineering and data science who can influence platform strategy and standards.
Strong in building scalable custom AI/ML services and integrating SaaS/PaaS AI products within enterprise settings.
Able to communicate complex technical concepts strategically to business stakeholders and model business trade-offs (TCO vs. NPV).