Develops orchestration and communication layers between agents using frameworks like A2A SDK, LangGraph, or Auto Gen
Integrates AI agents with toolchains such as Jira, Bitbucket, Github, Terraform and monitoring platforms
Collaborates on system design, SDK development and data pipelines supporting agent intelligence
Provides technical leadership, mentorship, and guidance to junior engineers and team members.
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years applied experience.
Experience in Software engineering using AI Technologies
Strong hands-on skills in Python, Pydantic, FastAPI, LangGraph, and Vector Databases for building RAG based AI agent solutions integrating with multi-agent orchestration frameworks and deploying end-to-end pipelines on AWS (EKS, Lambda, S3, Terraform)
Experience with LLMs integration, prompt/context engineering, AI Agent frameworks like Langchain/LangGraph, Autogen, MCPs, A2A.
Solid understanding of CI/CD, Terraform, Kubernetes, Docker and APIs
Familiarity with observability and monitoring platforms
Strong analytical and problem-solving mindset.
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
Preferred qualifications, capabilities, and skills
Experience with Azure or Google Cloud Platform (GCP).
Familiarity with MLOps practices, including CI/CD for ML, model monitoring, automated deployment, and ML pipelines.
