Lead AI Engineer

Role Summary

We are seeking a highly skilled Lead AI Engineer to build and deploy Agentic AI solutions that fundamentally accelerate our Product Development Life Cycle (PDLC). Partnering directly with the Engineering Process Improvement Lead, your objective is to translate identified operational bottlenecks into production-ready AI automations. You will be hands-on, writing code and architecting multi-agent systems that autonomously handle everything from Jira story generation to backend code scaffolding, PR reviews, and pre-production deployments. You will ensure these AI-driven workflows scale seamlessly across our distributed engineering teams in the US, India, and other global regions, while strictly adhering to enterprise compliance and capitalization standards. The role spans not only AI-assisted software implementation, but also the use of agentic systems to support production investigation, operational response, engineering analysis, and other high-leverage engineering workflows across the software development and delivery lifecycle. A key part of the role is designing effective human-in-the-loop systems that allows engineers to supervise, guide and approve the work of multiple AI agents operating together to execute an orchestrated business workflow.

Key Responsibilities

Agentic AI Architecture & Development

Design, build, and deploy multi-agent systems using frameworks like AgentCore, LangGraph, AutoGen, or CrewAI to automate complex software engineering workflows.

Integrate LLMs (e.g., GPT-4, Claude, Gemini) with internal developer tooling via APIs to execute semi-autonomous and autonomous tasks.

Develop tools and functions for AI agents to interact safely with our source code repositories, CI/CD pipelines, and project management systems.

Focus on deploying secure solutions, with a focus on sandboxing and least privilege

Global PDLC Automation & Execution

Build the pipelines that achieve the target state defined by Process Operations:

Develop NLP-driven pipelines to parse Product Requirement Documents (PRDs) and automatically generate structured, sprint-ready Jira stories via the Jira API.

Integrate AI coding assistants and visual AI tools to accelerate UX design and frontend UI code generation.

Engineer autonomous agents capable of collaborating with human developers on backend code, generating scaffolding, and writing unit tests.

Build automated, AI-driven Pull Request (PR) review bots to analyze code quality, logic errors, and security vulnerabilities before merging.

Automate the promotion and deployment of approved code to pre-production environments using CI/CD tools in partnership with our Technical Operations teams

Global Engineering Enablement: Design AI automations that accommodate asynchronous development cycles, ensuring smooth code hand-offs and continuous CI/CD operations between US and India time zones.

Compliance & Financial Tracking Automation

Implement strict systemic guardrails within the AI agents to ensure generated code and automated deployments comply with FedRAMP, SOC 2 Type 2, and ISO27001 requirements across all geographic deployments.

Engineer automated metadata tagging within the agentic workflows (e.g., auto-labeling Jira tickets and GitHub PRs) to systemically categorize R&D (CapEx) vs. Operational (OpEx) work for accurate software capitalization reporting globally.

Required Qualifications

8+ years of hands-on software engineering experience, with a strong background in backend development (Java, Python, Go, or Node.js) and SRE/DevOps practices.

Proven experience building applications using LLMs and Agentic AI frameworks (LangChain, LangGraph, AutoGen, LlamaIndex, Vertex etc.).

Deep expertise interacting with REST/GraphQL APIs for enterprise developer tools (Jira, GitHub/GitLab, Figma, CI/CD platforms).

Strong background in deployment automation, Infrastructure as Code (Terraform, CloudFormation), and container orchestration (Kubernetes) in multi-cloud environments.

Global Team Experience: Demonstrated success in collaborating with and building internal developer tools for multi-geography engineering teams (specifically US and India), including managing asynchronous workflows.

Practical knowledge of implementing security scanning and compliance checks (FedRAMP, SOC2) directly into automated CI/CD pipelines.

Ability to take high-level process improvement requirements and translate them into robust, fault-tolerant engineering automations.