Back
Tier-1 brand and metro mid-level role, but niche LLM/agent requirements limit applicant pool.
Specialized LLM, agent orchestration, and vector DB expertise makes cross-industry transfer limited.
Multiple mandatory years plus specific LLM, Python, AWS, Terraform, and orchestration tool requirements.
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 AI-native SDLC agent framework using multi-agent systems, LLM orchestration, and automation on AWS.
Develop and integrate AI agent services for code generation, test creation, observability, and toolchain integration with platforms like Jira, Bitbucket, GitHub, and Terraform.
Provide technical leadership, mentor engineers, and drive adoption of AI-assisted engineering practices with measurable standards to improve code quality, delivery speed, and operational outcomes.
5+ years of applied software engineering experience with formal training or certification in software engineering concepts.
Strong hands-on experience in Python, Pydantic, FastAPI, LangGraph, vector databases, and 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 solid knowledge of CI/CD, Terraform, Kubernetes, Docker, and APIs.
Work Experience Required: 5+ years; Notice Period: Not explicitly mentioned in the JD.
Experienced in building and leading AI-driven software engineering automation frameworks with a focus on SDLC toolchain integration and AI agent orchestration.
Comfortable operating in cloud-native environments (AWS preferred) with deep knowledge of AI/ML technologies and responsible AI practices.
Demonstrated ability to mentor senior engineers on compliant, secure, and resilient AI usage, and to establish best practices and governance for AI-assisted development workflows.