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Mid-level ML/GenAI role with common title and metro hiring but specialized skillset reduces applicants.
Role requires deep GenAI, data platform, and cloud expertise, limiting cross-industry transferability.
Mandatory 5+ years plus many required AWS, Databricks, vector search, and Kubernetes skills.
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Own end-to-end delivery and scaling of a GenAI and data platform on AWS enabling LLM-powered capabilities including vector search, graph databases, and data pipelines.
Design, build, and maintain backend AI services, secure APIs, scalable data pipelines, and CI/CD pipelines for production AI workflows.
Lead development of agentic AI system capabilities including tool integration, memory/state management, security guardrails, and innovation in LLM workflows.
5+ years of experience in software engineering, data engineering, or AI/ML engineering.
Proficiency in Python and hands-on AWS experience with services like OpenSearch, Neptune, DynamoDB, ElastiCache.
Experience with Databricks or Apache Spark, backend service and API development, Docker, Kubernetes, and CI/CD pipelines.
Work Experience Required: 5+ years as explicitly mentioned in the JD.
Experienced in building and operating production-grade AI/ML platforms focused on LLM and vector search technologies within cloud environments (AWS).
Skilled in integrating multiple AI frameworks (e.g., LangChain, LangGraph) and implementing secure, observable, and scalable AI systems in regulated or compliance-sensitive environments.
Comfortable with innovation and driving agentic AI capabilities, responsible for evolving AI tooling and platform towards advanced agent-based architectures.