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Tier-1 brand, metro location, mid-level ML/AI generalist role with broad skills attracts many applicants.
Specialized LLM, agent-framework, and responsible AI requirements reduce cross-industry transferability.
Explicit 5+ years and mandatory LLM, infra, and cloud skills make filters stringent.
Lead design and implementation of LLM-driven AI agent services and orchestration layers on AWS, integrating with multi-agent frameworks and software toolchains.
Provide technical leadership and mentorship to software engineers, driving adoption and governance of AI-assisted engineering practices across SDLC to improve code quality and delivery speed.
Collaborate on system design, SDK development, and build automation solutions to create a self-optimizing software delivery lifecycle ecosystem.
5+ years of applied software engineering experience with formal training or certification in software engineering concepts.
Strong hands-on skills in Python, Pydantic, FastAPI, LangGraph, Vector Databases, and experience deploying AI agent solutions on AWS (EKS, Lambda, S3, Terraform).
Experience with LLM integration, prompt engineering, AI agent frameworks (Langchain, LangGraph, Autogen, MCP, A2A), and knowledge of CI/CD, Terraform, Kubernetes, Docker, and APIs.
Demonstrated expertise in enterprise-authorized AI-assisted software development tools usage and governance, including responsible AI practices and security considerations.
Experienced in architecting and building AI-native software delivery lifecycle frameworks leveraging multi-agent systems and LLM orchestration.
Proven ability to lead and coach engineering teams on AI-assisted development processes and responsible AI usage within complex enterprise environments.
Strong background in cloud-native development on AWS with familiarity or openness to extend skills into Azure or GCP and MLOps practices.