Salary
≈ $8.5k – $22k per year (Estimated)
Location
In office (Noida)
Seniority
Junior · 2+ years exp
Confirmed on the employer's own hiring board on Oct 26, 2026. First seen by Alion on Mar 10, 2026. EXL scores B on the Alion truth index.
Overview
Company
Impact
Profile match
EXL, legally ExlService Holdings, is a data analytics, AI and digital operations company headquartered in New York that runs outsourced business processes and builds data and AI solutions for insurers, healthcare organisations, banks, media and retail companies. Founded in 1999 and listed on Nasdaq, it has more than 60,000 employees across six continents, with large delivery centers in Noida, Gurgaon, Pune, Bengaluru and Chennai and a newer AI hub in Dublin. It hires data scientists and data engineers, GenAI and MLOps engineers, analytics managers, solution consultants and client partners, plus talent acquisition, finance and medical coding staff.
AI Engineer, India, 2-3 years
- Design, build, and optimize agentic AI systems leveraging LLMs, multi-agent frameworks, tool-use orchestration, retrieval systems, and autonomous workflows to solve complex real-world problems.
- Develop scalable ML pipelines and infrastructure including data processing, feature engineering, model training/evaluation and deployment using modern ML-Ops practices (Docker, Kubernetes, CI/CD, observability).
- Integrate agents with enterprise tools and APIs enabling autonomous planning, reasoning, and execution chains across cloud platforms, internal systems, and third-party services.
- Collaborate cross-functionally with product, engineering, and domain SMEs to design end-to-end intelligent workflows, ensure reliability/safety, and translate business needs into production-grade agentic solutions.
- Minimum 2 year(s) of hands-on experience with Python (comfortable with PyTorch/TensorFlow)
- Minimum 1 year(s) of experience with LangChain/LlamaIndex
- Minimum 1 year(s) of experience with RAG pipelines and vector databases
- Practical exposure to agentic/LLM systems such as tool-calling, autonomous task execution, workflow orchestration, reasoning chains, prompt engineering, and integrating models with APIs or enterprise systems.
- Strong MLOps & engineering discipline including experience with cloud platforms (Azure/AWS/GCP), containerization (Docker), CI/CD, data pipelines, and deploying ML systems into production environments.
- Problem-solving mindset and cross-functional communication skills with the ability to translate business requirements into intelligent workflows, run experiments, and iterate quickly in a fast-paced AI environment.
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