Data Science GenAI Engineer

Reposted 19 Days Ago
Be an Early Applicant
Gurugram, Haryana, IND
Hybrid
Mid level
Information Technology • Database • Consulting
The Role
Design, develop, and deploy Generative AI applications using LLMs, RAG, prompt engineering, and fine-tuning pipelines. Integrate models into production through APIs, microservices, and cloud platforms. Evaluate model performance, reduce hallucinations, and improve accuracy, safety, and latency. Implement monitoring, logging, feedback loops, and MLOps practices while collaborating with product, data science, and engineering teams. Ensure solutions follow security, privacy, and responsible AI guidelines.
Summary Generated by Built In
  • 4–6 years of overall software engineering or ML experience.
  • 2+ years of hands-on experience in Generative AI, LLMs, or applied NLP.
  • Strong programming skills in Python (required); experience with JavaScript/TypeScript is a plus.
  • Practical experience with:
    • LLM APIs and frameworks (e.g., OpenAI-compatible APIs, LangChain, LlamaIndex)
    • Prompt engineering and RAG architectures
    • Vector databases (e.g., Pinecone, FAISS, Weaviate, Chroma)
  • Experience deploying ML/AI solutions in cloud environments (AWS, Azure, or GCP).
  • Solid understanding of data structures, APIs, and system design.
  • Ability to write clean, maintainable, and testable code.
Responsibilities
  • Design, develop, and deploy Generative AI applications using LLMs (e.g., GPT-based, open-source models).
  • Build and optimize prompt engineering, RAG (Retrieval-Augmented Generation), and fine-tuning pipelines.
  • Integrate GenAI models into production systems using APIs, microservices, and cloud platforms.
  • Collaborate with product managers, data scientists, and backend engineers to translate business needs into AI-driven solutions.
  • Evaluate model performance, mitigate hallucinations, and improve accuracy, safety, and latency.
  • Implement monitoring, logging, and feedback loops for continuous model improvement.
  • Stay current with emerging GenAI frameworks, tools, and best practices.
  • Ensure compliance with security, privacy, and responsible AI guidelines.
Qualifications
  • Experience in RAG based applications using LangGraph and FastAPI.
  • Familiarity with MLOps practices (CI/CD for ML, model versioning, monitoring).
  • Knowledge of NLP concepts such as embeddings, transformers, and tokenization.

Skills Required

  • 4–6 years of overall software engineering or machine learning experience
  • 2+ years of hands-on Generative AI, LLM, or applied NLP experience
  • Strong Python programming skills
  • Experience with JavaScript or TypeScript
  • Practical experience with LLM APIs and frameworks such as OpenAI-compatible APIs, LangChain, and LlamaIndex
  • Experience with prompt engineering and RAG architectures
  • Experience with vector databases such as Pinecone, FAISS, Weaviate, or Chroma
  • Experience deploying machine learning or artificial intelligence solutions in AWS, Azure, or GCP cloud environments
  • Understanding of data structures, APIs, and system design
  • Ability to write clean, maintainable, and testable code
  • Experience building RAG applications using LangGraph and FastAPI
  • Familiarity with MLOps practices, including ML CI/CD, model versioning, and monitoring
  • Knowledge of NLP concepts including embeddings, transformers, and tokenization

EXL Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about EXL and has not been reviewed or approved by EXL.

  • Healthcare Strength — Medical plan options include HSA, HRA, and PPO choices, with additional health-related programs such as disability coverage and an employee assistance program. Feedback suggests these options provide meaningful flexibility and coverage breadth for many roles.
  • Leave & Time Off Breadth — Up to 20 days of paid time off, plus federal holidays and paid sick and safe time, are highlighted. Feedback suggests this leave structure is a notable benefit alongside core compensation.
  • Retirement Support — A traditional and Roth 401(k) with up to a 3% company match and full vesting after one year is described. Feedback suggests this match and vesting timeline help bolster long-term savings.

EXL Insights

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The Company
HQ: New York, NY
30,246 Employees
Year Founded: 1999

What We Do

Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.

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