Sr Lead Software Engineer - Agentic AI

Next Frontier Capital; Bengaluru; On-site; INR 400,000 - 700,000; Full time

Job summary

Next Frontier Capital in Bengaluru invites a seasoned software engineer to lead AI-driven engineering initiatives. You will design and implement LLM-driven agent services, build end-to-end AI pipelines on AWS, and mentor junior engineers.

You will collaborate with product and operations to deliver robust, secure, and scalable AI solutions across multiple teams. The role emphasizes governance of AI-assisted practices, experience with multi-agent orchestration, and strong hands-on skills in

Qualifications

• Formal training or certification on software engineering concepts with 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 deploying end-to-end pipelines on AWS.
• Experience with LLMs integration and AI Agent frameworks like Langchain/LangGraph/Autogen.
• Solid understanding of CI/CD, Terraform, Kubernetes, Docker and APIs.
• Familiarity with observability and monitoring platforms.
• Strong analytical and problem-solving mindset.
• Experience leading the use of enterprise AI tools with governance and secure practices.

Responsibilities

• Works closely with software engineers, product managers, and other stakeholders to define requirements and deliver robust solutions.
• Designs and Implement LLM-driven agent services for design, code generation, documentation, test creation and observability on AWS
• 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.

Skills

Python

Pydantic

FastAPI

LangGraph

Vector Databases

LLMs integration

Langchain

Autogen

A2A

CI/CD

Terraform

Kubernetes

Docker

APIs

Observability

Problem solving

Education

Software engineering certification

Tools

A2A SDK

LangGraph

Langchain

Autogen

MCPs

Terraform

EKS

S3

Docker

Job description

Job responsibilities

• Works closely with software engineers, product managers, and other stakeholders to define requirements and deliver robust solutions.
• Designs and Implement LLM-driven agent services for design, code generation, documentation, test creation and observability on AWS
• 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.

Drive significant business impact and tackle a diverse array of challenges that span multiple technologies and applications