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Reputable bank brand and Bangalore metro increase applicants, but senior niche Agentic AI reduces density.
Strong specialization in Agentic AI, LLM evaluation, and cloud infrastructure requires specific ML/AI background.
Explicit 10-15 years plus mandatory Agentic AI, DevOps, and AWS skills create stringent screening.
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Design, build, and deliver production-grade Agentic AI solutions using Python, Agent SDKs, and LangGraph, including multi-agent workflows and autonomous reasoning systems.
Develop and maintain backend services/APIs to support agentic capabilities and implement observability, tracing, and evaluation frameworks (LangFuse, HoneyHive).
Lead technical standards, DevOps practices (CI/CD, containerisation, IaC), AWS infrastructure management, code reviews, mentoring, and drive adoption of emerging Agentic AI tools.
10-15 years of experience in advanced Python engineering with expertise in FastAPI/Flask/Django, TDD, RESTful APIs, microservices, and application security (OAuth2/JWT, OWASP).
Proficiency in DevOps tools: GitHub Actions/Jenkins, Docker, Kubernetes/ECS Fargate, Terraform/CDK, AWS services including ECS, Lambda, S3, RDS, API Gateway, Bedrock.
Experience with Agentic AI tools and frameworks including LangGraph, LangChain, Agent SDKs (OpenAI/Bedrock), CrewAI/AutoGen, LangFuse, HoneyHive for monitoring and evaluation.
Bachelor's degree in Engineering or equivalent; Location requirement: Bangalore.
Senior engineer with deep expertise in Python and Agentic AI systems capable of leading multi-agent orchestration and autonomous AI workflows.
Experienced in full lifecycle AWS cloud infrastructure design and DevOps best practices to ensure scalable, secure, and maintainable solutions.
Technical leader who mentors engineers, drives high-quality code standards, and champions adoption of advanced AI and observability tools within a corporate technology environment.