Job Description

Position Title, Responsibility Level

Function

Enterprise AI

Reports to

VP / SVP – Enterprise AI

Permanent/ Temporary

Permanent

Experience

6-10 Years

Location

NCR/Bengaluru/Hyderabad

Role Summary (Strategic Mandate)

• Business Analysis of As-Is to Reimagined Workflows

• Design Solution Features & Functionalities

• Solution Lifecycle Management: Discovery-Prototype-Production Delivery-Enhancements-Scaling-DevSecOps

• Operations & Functional Stakeholders’ Interfacing

• Accountability of Business Case definition, metrics, targets and realisation

• Solution Packaging, Business & Client’s Showcasing

• Aligned to the Forward Deployment Engineering (FDE), who is a hands-on, stakeholder-facing who operates at the intersection of engineering, product, and delivery – embedding directly with functional teams to re-imagine, discover, and design AI solutions for production environments

Key Responsibilities

1. AI-First Solution Engineering

• Work with stakeholders’ and EXL delivery teams to deeply understand business workflows, data landscapes, and systems, and translate business problems into technical AI solution designs.

• Rapidly build AI prototypes, POCs, and pilots, and take them through to production deployment and enterprise adoption.

• Configure, customize, and extend EXL’s AI platforms and accelerators to fit business-specific contexts and constraints.

2. Agentic AI & AI Solution Development

• Design and build agentic workflows, advances RAG pipelines, LLM integrations, tool-using agents, and human-in-the-loop systems.

• Apply prompt engineering, evaluation frameworks, and fine-tuning techniques to achieve accuracy, reliability, and safety targets.

• Integrate AI solutions with enterprise systems (APIs, data platforms, CRMs, contact center and core operations systems).

3. Deployment, Integration & Production Ownership

• Own end-to-end deployment: environment setup, CI/CD, LLMOps/MLOps, monitoring, guardrails, and observability.

• Ensure solutions are secure, compliant, and production-grade, aligned with responsible AI, privacy, and EXL security standards.

• Troubleshoot live systems and continuously optimize for latency, cost, quality, and scale.

4. Stakeholder Engagement & Value Realization

• Act as a trusted technical advisor to stakeholders; lead demos, workshops, and technical deep dives.

• Define success metrics and demonstrate measurable outcomes: productivity gains, cost reduction, risk mitigation, and revenue uplift.

• Capture field feedback and channel it into EXL’s platform and product roadmaps.

5. Collaboration & Knowledge Sharing

• Collaborate closely with platform engineering, data science, and delivery teams across geographies.

• Create reusable assets, accelerators, playbooks, and documentation from multiple deployments.

• Mentor engineers and evangelize best practices in applied and agentic AI across accounts.

Technical & Architecture Expertise (Agentic AI)

• Strong hands-on software engineering skills (Python and/or TypeScript), including APIs, microservices, and cloud-native development on AWS, Azure, or GCP.

• Practical experience building with agentic AI frameworks such as LangGraph, LangChain, CrewAI and emerging standards like MCP (Model Context Protocol).

• Deep working knowledge of foundation models and platforms, including OpenAI, Anthropic Claude, and open-source LLM stacks; strong grasp of RAG, embeddings, and vector databases.

• Experience with LLMOps/MLOps: evaluation, monitoring, guardrails, versioning, and cost/performance optimization in production.

• Exposure to multimodal AI technologies (voice, avatar, video) using platforms like ElevenLabs and HeyGen.

• Fluency with AI-native developer tools such as Cursor, Replit, Claude Code, and related copilots.

• Ability to design solutions within enterprise-grade architecture, security, and responsible AI guardrails, balancing scalability, cost, and compliance.

Key Outcomes & Success Metrics

• High POC-to-production conversion and rapid deployment velocity across engagements.

• Tangible business outcomes – productivity, cost, quality, and revenue impact – attributable to deployed solutions.

• Reusable solution assets and accelerators adopted across multiple accounts, not one-off builds.

• Strong production reliability, security, and responsible AI compliance posture for deployed solutions.

• High satisfaction and expansion of EXL’s AI footprint within assigned accounts.

Required Experience & Qualifications

• 4–8 years of experience in software engineering, applied AI/ML, or solution engineering, with 2+ years of hands-on GenAI/LLM development.

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience).

• Proven track record of:

  • Shipping production GenAI/agentic AI solutions for enterprise functions

  • Working directly with business stakeholders in consulting, forward-deployed, or solution engineering roles

  • Delivering measurable business outcomes from AI deployments

• Strong understanding of:

  • Agentic AI and LLM architectures, RAG, and evaluation

  • Data platforms, cloud, security, and compliance

  • Enterprise integration and legacy system modernization

• Willingness to travel and work onsite at business locations as required.