AI Architect - Generative AI & Enterprise Solutions

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:

This is a senior, highly visible role that blends deep technical mastery with executive influence. You are expected to be a recognized expert in generative AI – frameworks, agent harnesses, and the realities of production deployment – and equally comfortable in the boardroom, translating complex technology into clear business value for CXO-level stakeholders and helping shape enterprise AI strategy.

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field – or equivalent practical experience.
• 10+ years of experience in software/AI engineering and architecture, including senior technical leadership on large-scale systems.
• Recognized depth in generative AI: LLMs, prompt/context engineering, RAG, agent frameworks (e.g., LangChain, Amazon Bedrock Agents), and agent harnesses.
• Proven track record designing and running GenAI solutions in production at scale – including evaluation, observability, cost, and reliability.
• Deep hands-on knowledge of AWS and its AI services (e.g., Amazon Bedrock), plus core cloud infrastructure (compute, networking, IAM, containers).
• Strong grounding with enterprise architecture practices, integration, and the modern DevOps toolchain (GitHub, Jenkins, Artifactory, SonarQube).
• Exceptional communication and executive-presence skills – able to hold credible, persuasive CXO-level conversations and articulate complex technology in business terms.
• Solid understanding of responsible-AI, security, and data-governance considerations for enterprise AI.

Architecture & Technical Leadership

• Drive the end-to-end architecture and technical vision for generative AI within the function – reference architectures, patterns, and standards that teams build against.
• Make authoritative technology decisions, selecting the right models, frameworks, and agent harnesses for each use case, balancing capability, latency, cost, and risk.
• Move solutions from proof-of-concept to production with realistic, production-grade designs – covering orchestration, retrieval (RAG), evaluation, observability, guardrails, and human-in-the-loop.
• Integrate GenAI into the existing cloud solutions and automation platform (AWS, CI/CD toolchain, ITSM) so GenAI is a first-class, governed capability.

Running AI at Scale

• Design for scale and operational excellence for GenAI workloads – throughput, latency, reliability, and cost optimization (token economics, caching, model routing).
• Establish the operational foundation including evaluation pipelines, monitoring, drift/quality management, and incident response for Agentic solutions.
• Bake in guardrails such as security, data privacy, responsible-AI, hallucination mitigation, and regulatory compliance into every architecture.

Executive Engagement & Strategy

• Advise and influence to CXO-level leaders, translating complex AI concepts into clear business value, trade-offs, risks, and roadmaps.
• Shape the generative-AI strategy and roadmap for the enterprise, aligning technology investment with business outcomes and priorities.
• Build and present business cases, ROI, and build-vs-buy analyses for AI initiatives.
• Serve as an evangelist and trusted expert – to executives, engineering teams, and external partners – and champion an enterprise AI vision.

Enablement & Governance

• Mentor and upskill engineering teams and set architecture governance, review gates, and reusable building blocks.
• Define and steward AI governance, standards, and best practices in partnership with security, data, and legal.