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:
o Shipping production GenAI/agentic AI solutions for enterprise functions
o Working directly with business stakeholders in consulting, forward-deployed, or
solution engineering roles
o Delivering measurable business outcomes from AI deployments
Strong understanding of:
o Agentic AI and LLM architectures, RAG, and evaluation
o Data platforms, cloud, security, and compliance
o Enterprise integration and legacy system modernization
Willingness to travel and work onsite at business locations as required.
Leadership & Behavioral Expectations
Owner’s mindset with a strong bias for action and outcome orientation.
Comfort with ambiguity, rapid iteration, and fast-changing environments.
Exceptional communication skills – able to translate deep technical work into business value
for executives.
Collaborative, and AI-first working style across diverse teams.
Deep commitment to responsible AI and ethical deployment.
Pay: ₹3,000,000.00 - ₹3,500,000.00 per year
Work Location: In person
