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Tier-1 employer, metro location, and senior software title increase competition but GenAI specialization narrows applicants.
Specialized GenAI platform and LLMOps skills are transferable across industries but require AI-specific experience.
Extensive mandatory GenAI, LLMOps, cloud, Kubernetes, and production engineering skills create strict technical filters.
Lead and deliver moderately complex AI and GenAI-focused software engineering initiatives, including designing and deploying enterprise-scale AI-enabled applications and platforms.
Architect and develop AI agent workflows, model orchestration solutions, and reusable AI components using frameworks like LangChain, AutoGen, and cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI).
Collaborate across teams to build secure, scalable, and maintainable services with observability and compliance, deploying on container platforms such as Kubernetes or OpenShift.
Work Experience Required: Not explicitly mentioned in the JD
Proficiency in Python and experience with AI frameworks, REST APIs, asynchronous services, and microservices architectures.
Experience with AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI) and GenAI technologies such as LangChain, LangGraph, AutoGen, MCP, or equivalents.
Experience with cloud-native engineering including Kubernetes, containers, CI/CD automation, and secure, scalable application development.
Strong background in architecting and scaling enterprise GenAI and AI-driven platforms with deep domain expertise in agentic AI, multi-agent systems, and knowledge graphs.
Experience designing AI model orchestration (routing, selection, fallback), AI observability, security, governance, and cost optimization strategies.
Capable of leading moderately complex initiatives independently, collaborating with cross-functional teams, and mentoring less experienced staff in a large enterprise technology environment.