Back
Tier-1 brand, mid-level experience expectation, and metro hiring increase candidate density, moderated by niche agentic AI skillset.
Highly domain-specific ML/AI, LLM orchestration, and RAG experience reduce cross-industry transferability.
Explicit 5+ years requirement plus mandatory LLM, orchestration, cloud, and infra skills enforce high filtering.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead design and implementation of LLM-driven AI agent services to automate phases of the software delivery lifecycle on AWS.
Architect and develop orchestration layers between agents using frameworks such as A2A SDK, LangGraph, Auto Gen, integrating with toolchains like Jira, Bitbucket, Github, Terraform.
Provide technical leadership and govern adoption of AI-assisted engineering practices to improve code quality, delivery speed, and operational standards within SDLC toolchain.
Formal training or certification in software engineering concepts.
5+ years of applied software engineering experience.
Strong hands-on skills in Python, Pydantic, FastAPI, LangGraph, Vector Databases, AWS (EKS, Lambda, S3), Terraform, Kubernetes, Docker.
Experience integrating LLMs and AI agent frameworks (Langchain, LangGraph, Autogen, MCPs, A2A) and using enterprise-authorized AI-assisted development tools responsibly.
Experienced in building scalable AI-native SDLC solutions leveraging multi-agent systems and AI toolchains in cloud environments.
Technical leader comfortable mentoring engineers and setting governance for secure, compliant AI usage in software workflows.
Skilled in integrating complex AI orchestration with existing CI/CD pipelines and monitoring platforms to drive automation at scale.