Lead AI Engineer – Cloud Infrastructure, Automation

EXL

Lead AI Engineer defining and driving application of agentic AI across cloud infrastructure and automation platform. Architect agentic applications and workflows while mentoring engineers in AI systems.

Posted 7/22/2026full-timeNew York City • New Jersey, New York • 🇺🇸 United StatesSenior💰 $150,000 - $170,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building AI systems and infrastructure-as-code using Terraform, while applying intelligent automation and agentic techniques to enhance cloud operations. Proven ability to mentor engineers and set technical direction, ensuring compliance with security and responsible-AI standards.

Highest-signal resume keywords

Terraform Infrastructure-As-CodeAI Systems DesignAWS ArchitectureCI/CD Toolchain IntegrationAgentic Applications Development

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

AI Agent DevelopmentInfrastructure AutomationPolicy-As-Code ImplementationContext EngineeringRAG PipelinesAIOpsPrompt EngineeringModel SelectionAutomated ValidationMulti-Agent Systems

Soft Skills

Technical LeadershipMentoringCollaborationCommunicationDesign Review

Tools & Technologies

GitHubJenkinsArtifactorySonarQubeLangChainAmazon Bedrock AgentsITSM ToolsCloud Automation Platforms

Certifications & Qualifications

Master's Degree

Industry Keywords

Infrastructure-As-CodeCloud OperationsAI ComplianceResponsible-AISecurity Policy

Tech Stack

Tools & technologies

AWSCloudITSMJenkinsTerraform

About the role

Key responsibilities & impact

Your primary mandate is to accelerate delivery of business-unit solutions by building AI systems that generate, validate, and ship infrastructure-as-code – Terraform in particular – so environments are stood up faster and more consistently.
You will architect agentic applications and workflows on AWS, apply intelligent automation across the platform and cloud operations, and pioneer emerging agentic techniques.
As a technical leader, you will set direction, establish standards and guardrails, and mentor engineers while remaining hands-on with design and implementation.

Requirements

What you’ll need

Master's Degree
Design and build AI agents and tools that generate, validate, and refactor infrastructure-as-code – Terraform in particular – to accelerate the delivery of business-unit solutions.
Embed guardrails, policy-as-code, and automated validation into the generation workflow so generated infrastructure adheres to standards, security policy, and reusable module patterns before it reaches production.
Reduce provisioning lead time and rework by integrating AI-assisted code generation and review into the CI/CD toolchain (GitHub, Jenkins, Artifactory, SonarQube).
Curate and maintain a library of reusable Terraform modules, blueprints, and golden patterns that agents can compose to stand up new environments quickly and consistently.
Architect and build agentic applications and multi-agent systems using modern agent frameworks (e.g., LangChain, Amazon Bedrock Agents) to automate infrastructure, platform, and operations workflows.
Apply and advance emerging agentic techniques such as context engineering, agent harnesses, tool/function calling, and evaluation loops to improve agent reliability and autonomy.
Implement RAG pipelines (retrieval-augmented generation) over internal knowledge sources – runbooks, architecture docs, Terraform modules, and ITSM history – to ground agent behavior.
Establish patterns and standards for prompt/context engineering, model selection, evaluation, cost management, and responsible-AI guardrails (security, privacy, and hallucination controls).
Apply AIOps for anomaly detection, event correlation, and automated remediation across the cloud estate and automation platform.
Reduce mean-time-to-detect and mean-time-to-resolve by integrating AI into ITSM (ticket triage, classification, routing, and self-healing runbooks) to accelerate cloud operations.
Own the LLMOps/agent-ops foundation for agentic AI workloads – pipelines, deployment, evaluation, monitoring, and lifecycle management – on AWS.
Partner with security, data governance, and legal teams to ensure AI solutions meet compliance and responsible-AI requirements.
Set technical direction, define the AI roadmap for the platform, and provide architecture governance.
Mentor engineers, run design reviews, and grow agentic-AI capability across the team.

Benefits

Comp & perks

For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits