Role Summary (Strategic Mandate)
AI and Agentic Systems and Platform Architecture, Standards Development
Design Connected-Secure-Governed-Scalable Enterprise & Operations Solutions’
Components and Platform’s Fabric-Bus
Convene AI Architecture Reviews, Reference Architecture(s), Evaluation of Build vs. Buy
Considerations, Documentation of Choices, Subscription-Licensing Economics
Functional-Secure-Scalable-Governed Multi-Modal Systems, Drive Cross-Functional
Reusability, Guardrails, Pipelines
Guide Engineering & Runtime Delivery Teams, Address Complex Architectural Challenges
Interface with CTO-CIO stakeholders on Architectural Deliberations
Deep knowledge of Leading-Edge and Emerging AI Concepts and Capabilities: Knowledge
Graphs, Context Engineering, Agents Harness, and Loop Engineering
Understanding of Multi-Modal Ecosystem, Cloud, Data Mgmt., Responsible & Secure AI,
Token Economics, AI FinOps
Key Responsibilities
1. 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.
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.
Key Outcomes & Success Metrics
Robust, scalable agentic architectures that move engagements from POC to enterprise-
wide production adoption.
Reference architectures, patterns, and accelerators reused across multiple accounts –
reducing time-to-value and delivery risk.
Tangible business outcomes (productivity, cost, quality, revenue) enabled by sound
architecture decisions.
Strong security, compliance, and responsible AI posture across all designed solutions.
Recognized technical credibility with CTO/CIO organizations, contributing to account
growth and strategic deal wins.
Required Experience & Qualifications
12+ years of experience in software/solution architecture, data, or digital transformation,
with 3+ years architecting AI/LLM or agentic AI solutions.
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
Proven track record of:
o Architecting and delivering production AI/GenAI solutions for large enterprise
clients
o Serving as design authority across multiple concurrent engagements or programs
o Operating in business-facing, consulting, or forward-deployed environments with
senior stakeholders
Strong understanding of:
o Agentic AI and LLM architectures, RAG, evaluation, and guardrails
o Data platforms, cloud, security, and compliance
o Enterprise integration and legacy modernization
Experience engaging with CTOs, CIOs, enterprise architects, and executive
stakeholders.
Willingness to travel and work onsite at business locations as required.
Leadership & Behavioral Expectations
Enterprise-first mindset with strong commercial orientation and ownership of outcomes.
Ability to influence without authority across business organizations, delivery teams, and
partners.
Exceptional executive communication – able to explain and defend architecture decisions in
business terms to C-suite audiences.
Calm, decisive technical leadership in ambiguity, escalations, and rapid change.
Deep commitment to responsible AI and ethical deployment.
Pay: ₹3,000,000.00 - ₹3,500,000.00 per year
Work Location: In person
