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Senior Machine Learning Engineer

Senior Machine Learning Engineer

Capgemini
Fresher
Not Disclosed
Early Applicant
  • Posted 14 hours ago
  • Be among the first 20 applicants

Job Description

Your Role

  • Design and develop enterprise-grade Generative AI applications using LLMs.Build and optimize Retrieval-Augmented Generation (RAG) solutions.
  • Develop AI agents and multi-agent workflows using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar.
  • Integrate AI models with enterprise systems, APIs, databases, and knowledge repositories.
  • Fine-tune, evaluate, and optimize foundation models for business use cases.

Your Profile

  • Strong experience with OpenAI GPT, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar models.
  • Expertise in Prompt Engineering and Context Engineering.Hands-on experience with RAG pipelines and vector databases.
  • Experience with fine-tuning, model evaluation, and inference optimization.
  • Knowledge of AI guardrails, hallucination mitigation, and responsible AI practices.

What you love about working here

  • We offer a range of career paths and internal opportunities within Capgemini group.
  • You will get comprehensive wellness benefits including health checks, telemedicine, insurance with top-ups, elder care, partner coverage or new parent support via flexible work.
  • At Capgemini, you can work oncutting-edge projectsin tech and engineering with industry leaders or createsolutionsto overcome societal and environmental challenges

More Info

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Key Skills

vector databases

Claude

Azure OpenAI

Context Engineering

LangGraph

AI guardrails

model evaluation

Retrieval-Augmented Generation (RAG)

Mistral

RAG pipelines

responsible AI practices

LangChain

CrewAI

Generative AI

fine-tuning

OpenAI GPT

LLMs

Semantic Kernel

AutoGen

inference optimization

Llama

hallucination mitigation

Prompt Engineering

About Company

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Bengaluru, India, Remote
Skills:
Kubernetes, Sql, Java, Tableau, AWS, Kotlin, Python, Sap Business Objects, Docker, Gcp, Nlp, Spark, transformer architectures, benchmarking datasets, Go, Looker, PEFT, model alignment, LLM techniques, R-Shiny, LoRA, evaluation pipelines, Microstrategy, prompt engineering