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IN_Sr Associate__Generative AI Engineer

Pricewaterhouse Coopers Service Delivery Center Kolkata
Posted on
Pricewaterhouse Coopers Service Delivery Center Kolkata logo

Experience
2 - 5 yrs
Job Location
Bengaluru, India
Vacancy
1
Designation
Senior Machine Learning Engineer
Job Type
Not specified

Job Description

Job Description & Summary

We are seeking a highly skilled and innovative GenAI Engineer to join our dynamic team. The ideal candidate will be responsible for designing, developing, and deploying scalable Generative AI solutions using state-of-the-art large language models (LLMs) and transformer architectures. This includes building intelligent applications, orchestrating model workflows, and integrating GenAI capabilities into enterprise systems.

Responsibilities

  • Design, build, and deploy generative AI solutions using LLMs such as OpenAI, Anthropic, Mistral, or open-source models (e.g., LLaMA, Falcon).
  • Fine-tune and customize foundation models using domain-specific datasets and techniques
  • Develop and optimize prompt engineering strategies to drive accurate and context-aware model responses.
  • Implement model pipelines using Python and ML frameworks such as PyTorch, Hugging Face Transformers, or LangChain.
  • Agentic AI implementation expertise using C rew.ai or Lang chain
  • Collaborate with data engineers and MLOps teams to productionize GenAI models on cloud platforms (Azure/AWS/GCP).
  • Knowledge on Azure/GCP/AWS AI platform like Azure AI Foundry or GCP Vertex
  • Ensure robustness, scalability, and compliance of AI models in deployment environments.
  • Good to have experience in finetuning models
  • Good to have experience in SLM
  • Integrate GenAI into enterprise applications via APIs or custom interfaces.
  • Evaluate model performance using quantitative and qualitative metrics, and improve outputs through iterative experimentation.

Requirements

  • Proficient in using orchestration frameworks like LangChain, developing APIs with FastAPI or Flask, and managing ML pipelines using tools such as MLflow or Weights & Biases.
  • Familiarity with CI/CD practices for ML, including platforms like Azure ML or SageMaker Pipelines, is essential.
  • Knowledge on Azure/GCP/AWS AI platform like Azure AI Foundry or GCP Vertex.
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