GenAI / Agentic Architect

• Consulting Skills: High-impact executive presence; extensive experience in technology consulting, solution scoping, and technical proposal architecture.

• Senior Technical Architecture Mastery: Deep hands-on background in enterprise integration patterns. Expert-level capability in comparing orchestration layers (e.g., LangChain vs. LlamaIndex vs. Semantic Kernel) and evaluating modern execution environments (vLLM, TensorRT-LLM).

• Cloud & Vector Strategy: Deep familiarity with dictating enterprise data storage choices by comparing managed vector services (Pinecone) vs. distributed open-source engines (Milvus/Qdrant) or extending existing relational systems (pgvector).

• Qualifications: Master’s in computer science, AI, or IT Management; 12–18 years of progressive IT/AI experience, heavily indexing on senior technical architecture and client delivery.

The typical base pay range for this role across the U.S. is USD $200,000 - $280,000 per year.

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

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.

• Enterprise AI Strategy & Trade-Off Analysis: Define multi-year GenAI roadmaps by evaluating structural architecture trade-offs across the 7-layer LLM stack. Compare Commercial APIs (e.g., GPT-4o, Claude 3.5 Sonnet) vs. Hosted Open-Source LLMs (e.g., Llama 3) based on cost, latency, and data privacy.

• Advanced Architecture Oversight: Review and approve macro-level AI architectures. Architect scalable data ingestion pipelines, selecting between standard semantic RAG, Agentic RAG, or GraphRAG depending on enterprise complexity.

• Advanced Observability & Monitoring: Design deeply integrated AI observability (OBS) layers tailored for LLMs. Implement specialized tools (e.g., LangSmith, Phoenix, Arize) to track token consumption, trace complex agentic reasoning loops, and monitor model drift and hallucination rates in production.

• Robust Data Protection Strategy: Architect comprehensive data protection pipelines utilizing Enterprise Data Loss Prevention (DLP) tools. Ensure data is sanitized before hitting external APIs and implement robust semantic caching to prevent sensitive data leakage.

• Executive Consulting & Technical Pitching: Lead onshore client-facing engagements, pitching complex AI solutions to C-suite stakeholders, translating engineering trade-offs into clear financial and operational ROI frameworks.