Lead AI Engineer – Cloud Infrastructure & Automation

Work Location: NY/NJ Work Mode : Hybrid (2-3 days onsite) Pay Range :$150K-$170K /Yr Base + Annual Bonus The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits Job Overview: 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. AI-Driven Infrastructure Delivery 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. Agentic AI & LLM Applications 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). AIOps & Cloud Operations 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. Platform, Delivery & Leadership 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. AI-Driven Infrastructure Delivery 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. Agentic AI & LLM Applications 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). AIOps & Cloud Operations 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. Platform, Delivery & Leadership 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.