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Metro location plus broad ML/MLOps requirements increase applicant competition, balanced by senior 10+ years.
High because role requires deep ML, MLOps, vector search, and LLM specialization not easily transferable.
High due to explicit 10+ years and mandatory advanced ML, MLOps, and LLM/tooling requirements.
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Lead the design, development, and deployment of enterprise-scale AI/ML and Generative AI solutions, including scalable inference architectures and RAG-based applications.
Build and optimize end-to-end ML pipelines covering experimentation, deployment, monitoring, retraining, and lifecycle management with versioning and CI/CD automation.
Implement observability, governance, and production support for AI systems, and mentor engineering teams on AI/ML best practices.
10+ years of professional experience in AI/ML engineering.
Proven hands-on expertise across the full AI/ML lifecycle: data ingestion, feature engineering, model development, validation, deployment, monitoring, and retraining.
Strong technical skills in Python, SQL, APIs, and ML frameworks such as Scikit-learn, TensorFlow, PyTorch, Hugging Face, and LangChain.
Experience with MLOps tools (MLflow, DVC, Docker, Kubernetes, CI/CD pipelines), vector databases, RAG pipelines, semantic search, embeddings, and cloud-native scalable AI deployments.
Experienced in operating and scaling AI/ML systems in enterprise environments, capable of handling model lifecycle management including experimentation tracking and production support.
Strategic collaborator able to work with cross-functional teams and stakeholders to deploy AI-driven business solutions effectively.
Strong mentoring capability to establish engineering best practices and lead AI/ML engineering teams.