EXL

Analyzes business data and runs outsourced operations

Senior Manager

AI Application Engineering +1Confirmed live in the last 24 hours

Full-timeNoida, Uttar Pradesh, IndiaNo salary listedIn-person

About the Job

Requirements

Have 4–8 years of software engineering experience, including 1–2+ years of hands-on GenAI/LLM application development.
Provide a portfolio of shipped AI work, such as products, prototypes, GitHub projects, or demos that can be discussed.
Have a bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
Demonstrate fluency with AI-native development tools such as Cursor, Claude Code, Replit, and AI Studio through real projects, not just experimentation.
Demonstrate problem-solving skills and product sense, with attention to whether the work is used.
Communicate clearly in writing and verbally, and be comfortable demonstrating work to technical and business audiences.

Responsibilities

Build AI applications, copilots, and agentic workflows end-to-end, including user interfaces, APIs, business logic, and model integration.
Use rapid development tools such as Cursor, Claude Code, Replit, and Google AI Studio to shorten build cycles and iterate with users and stakeholders.
Turn loosely defined requirements into working demos and prototypes within days, then refine them based on feedback.
Develop with agentic SDKs and frameworks, including OpenAI Agents SDK, Anthropic Claude Agent SDK and API, Google Gemini and ADK, and LangChain or LangGraph.
Implement RAG pipelines, tool and function calling, structured outputs, and prompt engineering with systematic testing and evaluations.
Integrate models and agents with enterprise data sources and APIs, handling authentication, rate limits, and error paths.
Write clean, testable, well-documented code, using Git, containers, and CI/CD as standard practice.
Partner with Forward Deployment Engineers and platform teams to move successful prototypes into production, adding monitoring, guardrails, and cost controls.
Balance speed and quality pragmatically, deciding when to prototype quickly and when to harden the implementation.
Work with architects, data scientists, and designers; contribute to demos, accelerators, and internal hackathons.
Stay current with model and tooling developments and share learnings across the team.
Promote AI-assisted development practices that improve team velocity.
Deliver prototypes rapidly, with idea-to-prototype turnaround in days and prototype-to-production in weeks.
Ship applications, demos, and accelerators that are used by stakeholders and internal teams; maintain low defect rates, sensible test and evaluation coverage, and predictable cost, latency, and quality behavior.
Contribute reusable components, patterns, and utilities adopted by other engineers, and share AI-assisted development practices that improve team velocity.

Desired Qualifications

Experience with voice or multimodal tools such as ElevenLabs or HeyGen, MCP-based tool integration, fine-tuning, or open-source LLMs.

Key Responsibilities

1. Rapid Prototyping & Application Development

Build AI applications, copilots, and agentic workflows end-to-end – UI, APIs, business logic, and model integration.
Use rapid development tools (Cursor, Claude Code, Replit, Google AI Studio) to compress build cycles and iterate quickly with users and stakeholders.
Turn loosely-defined requirements into working demos and prototypes within days, then refine based on feedback.

2. Agentic & GenAI Engineering

Develop with agentic SDKs and frameworks – OpenAI Agents SDK, Anthropic Claude (Agent SDK / API), Google Gemini & ADK, LangChain/LangGraph.
Implement RAG pipelines, tool/function calling, structured outputs, and prompt engineering with systematic testing and evals.
Integrate models and agents with enterprise data sources and APIs, handling auth, rate limits, and error paths properly.

3. Engineering Quality & Productionization

Write clean, testable, well-documented code; use Git, containers, and CI/CD as standard practice.
Partner with Forward Deployment Engineers and platform teams to take successful prototypes into production, adding monitoring, guardrails, and cost controls.
Balance speed and quality pragmatically – knowing when to hack and when to harden.

4. Collaboration & Continuous Learning

Work closely with architects, data scientists, and designers; contribute to demos, accelerators, and internal hackathons.
Stay current with the fast-moving model and tooling landscape, and share learnings across the team.
Evangelize AI-assisted development practices that raise the whole team’s velocity.

Technical Skills & Tooling (Hands-On)

Rapid development tools as daily drivers: Cursor, Claude Code, Replit, Google AI Studio, GitHub Copilot – demonstrated ability to ship real software with AI-assisted workflows.
Agentic SDKs & frameworks: hands-on experience with OpenAI Agents SDK, Anthropic Claude APIs/Agent SDK, Google Gemini/ADK, and LangChain or LangGraph.
Strong programming skills in Python and/or TypeScript/JavaScript; comfort building full-stack prototypes (React/Node) and REST APIs.
LLM application patterns: prompt engineering, function/tool calling, structured outputs, RAG with vector stores (pgvector, Pinecone, FAISS, or similar).
Testing & observability basics: writing evals, using tracing tools (LangSmith, Langfuse, or similar), and monitoring cost/latency/quality.
Engineering foundations: Git, Docker, CI/CD, and at least one cloud (AWS/Azure/GCP).
Good to have: voice/multimodal experience (ElevenLabs, HeyGen), MCP-based tool integration, fine-tuning or open-source LLM experience.

4–8 years of software engineering experience, with 1–2+ years building GenAI/LLM applications hands-on.
A portfolio of shipped AI work – products, prototypes, GitHub projects, or demos you can walk us through.
Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent practical experience).
Demonstrated fluency with AI-native development tools (Cursor, Claude Code, Replit, AI Studio) in real projects – not just experimentation.
Strong problem-solving skills and product sense – you care about whether the thing you built actually gets used.

Clear written and verbal communication; comfortable demoing your work to technical and business audiences.

Speed of delivery: consistent idea-to-prototype turnaround in days and prototype-to-production in weeks.
Volume and quality of shipped work: applications, demos, and accelerators that are actually used by stakeholders and internal teams.
Reliability of what ships: low defect rates, sensible test/eval coverage, and predictable cost/latency behavior.
Contribution to reuse: components, patterns, and utilities adopted by other engineers.

Team velocity uplift through shared AI-assisted development practices.

About the Company

EXL

Analyzes business data and runs outsourced operations

EXL analyzes business data and runs outsourced operations for insurers, healthcare payers, banks, retailers, and other large organizations. Its teams combine industry expertise with data engineering and AI to handle tasks such as claims processing, payment reviews, customer service, and finance within existing technology environments. The company also builds software, including LifePRO, which manages insurance policies, and EXLdata.ai, which connects fragmented data for use in AI systems. Clients pay for consulting projects, ongoing managed services, and software delivered through the cloud. EXL was founded in 1999 and is headquartered in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1999

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