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.

 Solid 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

📌 AI Architecture Lead (Noida)
🏢 Acme Services
📍 Noida