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Data Science GenAI Engineer

EXL Talent Acquisition Team · Gurugram, Haryana, India · Hybrid

About EXL Talent Acquisition Team

303 open jobs

Weak hiring record · median 91 days open

9292

Hiring signal

Actively hiring

Still listed on the employer's own board · How we score

All reasons
  • 65% of this employer's openings are over 60 days old

In its favour

  • Still listed on the employer's own board

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Data Science GenAI Engineer at EXL Talent Acquisition Team. We’ll keep it in your applied jobs, so you can see it later and be warned before applying to the same employer twice.

  • 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.
  • 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.
  • 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

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