Key Responsibilities1. Agentic Solution Architecture & Design Authority Lead discovery and solutioning with stakeholders; translate business objectives into target-state agentic AI architectures, blueprints, and roadmaps. Own end-to-end solution design: multi-agent orchestration, tool-using agents, human-in-the-loop patterns, memory and state management, RAG and knowledge layers, and enterprise integration. Drive build vs. buy vs. partner decisions for models, agent frameworks, and solution with EXL standards.
2. Architecture Standards, Governance & Responsible AI Define and enforce reference architectures, design standards, and reusable patterns for agentic AI solutions across accounts. Embed security, privacy, compliance, and responsible AI – including agent guardrails, evaluation frameworks, and auditability – into every design. Conduct architecture and design reviews, ensuring solutions are scalable, cost-efficient, and production-grade.
3. Technical Leadership Through Delivery Guide Forward Deployment Engineers, data scientists, and delivery teams from design through production – remaining hands-on at critical points (prototyping, integration, performance tuning). De-risk delivery by resolving complex technical blockers: legacy integration, agent reliability, model performance, and latency/cost/quality trade-offs. Ensure solutions move beyond POCs to enterprise-wide adoption and value realization.
4. Stakeholder Engagement & Advisory Act as trusted technical advisor to CIOs, CDOs and enterprise architects; lead architecture workshops, design authority boards, and executive briefings. Support pre-sales and strategic deals: solution shaping, effort estimation, technical proposals, and orals. Articulate architecture decisions in business terms – value, risk, cost, and time-to-market.
5. Capability Building & Reuse Convert engagement learnings into reusable assets, accelerators, and reference implementations for EXL’s agentic AI portfolio. Mentor architects and senior engineers; raise the architecture bar across the Enterprise AI practice. Continuously track and translate emerging AI advances (Agentic AI, LLMs, autonomous systems) into EXL-ready architecture strategies.
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Technical & Architecture Expertise (Agentic AI)Multi-agent system design: supervisor–worker hierarchies, planner–executor and reflection loops, blackboard and swarm patterns; task decomposition, delegation, and inter-agent communication protocols; deciding when a single-agent vs. multi-agent topology is architecturally justified. Agent state, memory & context engineering: short-term vs. episodic vs. semantic memory design, checkpointing and resumability, durable execution for long-running agents; context-window budgeting, compaction/summarization strategies, and retrieval-augmented context assembly. Framework and protocol depth: LangGraph (graph state machines, interrupts, human-in-the-loop nodes), CrewAI, AutoGen/Semantic Kernel; MCP (Model Context Protocol) for tool and resource federation and A2A for agent interoperability; sound judgment on custom orchestration vs. framework adoption. Model strategy & token economics: model portfolio design and routing (frontier LLMs vs. SLMs), structured outputs and function-calling schema design, constrained decoding; fine-tuning vs. RAG vs. prompt-optimization trade-offs; prompt caching, batching, distillation, and quantization to hit latency and cost SLOs. Retrieval & knowledge architecture: hybrid retrieval (sparse + dense), rerankers, GraphRAG and knowledge graphs; chunking and embedding strategy, freshness pipelines, and access-control-aware retrieval (document/row-level security) for regulated enterprises. Evaluation architecture: golden datasets, LLM-as-judge with calibration, trajectory-level agent evals, regression harnesses wired into CI/CD gates, and online canary/A-B evaluation for continuous quality assurance. Guardrails, safety & governance: prompt-injection and jailbreak defenses, PII detection/redaction, policy engines, sandboxed tool execution, human-approval gates for high-risk actions, and full audit trails/lineage for responsible AI and regulatory compliance. Production & platform architecture: model gateways, multi-tenancy, VPC/private endpoints, HA/DR, autoscaling, rate limiting, and circuit breakers; observability via distributed tracing (OpenTelemetry), token/cost telemetry, and drift monitoring at enterprise scale. Enterprise integration: event-driven and API-led integration patterns, identity propagation (OAuth/OIDC), secrets management, and integrating agents with CRM, contact center, workflow platforms, and legacy estates. Multimodal & emerging stacks: voice agents (streaming ASR/TTS – e.g., ElevenLabs), avatar/video (HeyGen), computer-use agents; fluency with AI-native tooling (Claude Code, Cursor) and evolving OpenAI/Anthropic platform capabilities.
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