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AI Architect

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Onsite

AI Architect

Epam Systems

AI Architect

Skills

Google Cloud PlatformDeep LearningMachine LearningAIAWSComplianceAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Chennai, Tamil Nadu, India

Work mode:

Onsite

Published on:

4 September 2026

What they need


We are seeking a highly skilled AI Architect to lead the design, development, and deployment of cutting-edge Generative AI solutions, leveraging the latest tools, frameworks, and cloud platforms to solve complex challenges and drive innovation.

Responsibilities

  • Design and implement scalable architectures for Generative AI solutions using state-of-the-art tools and cloud platforms
  • Develop, fine-tune, and productionize Generative AI and traditional AI/ML models, ensuring optimal performance and stability
  • Ensure compliance with governance, security, and ethical AI standards in model development and deployment
  • Collaborate with cross-functional engineering teams to deliver seamless and robust AI solutions
  • Optimize model performance through effective evaluation methods, leveraging tools like RAGAS and DeepEval
  • Integrate vector databases such as FAISS and Qdrant into AI pipelines to enhance information retrieval and storage
  • Utilize cloud AI platforms, including AWS, GCP, or Azure, to build scalable solutions
  • Apply MLOps practices to streamline development and deployment workflows
  • Leverage Generative AI tools including LangChain, LlamaIndex, Hugging Face, PEFT, LoRA, QLoRA, and RAG pipelines for advanced solution-building
  • Provide expertise on traditional deep learning methods including LSTM, CNN, and VAE

Requirements

  • 12+ years of experience in AI/ML with at least 2 years specializing in Generative AI, LLMs, and agentic systems
  • Expertise in neural network architectures such as LSTM, CNN, and VAE
  • Proficiency in advanced GenAI tools such as LangChain, LlamaIndex, and Hugging Face
  • Skills in leveraging PEFT, LoRA, QLoRA, and RAG pipelines
  • Familiarity with vector databases including FAISS and Qdrant
  • Proficiency in cloud AI platforms such as AWS, Azure, and GCP, with a solid understanding of MLOps practices
  • Knowledge of AI evaluation frameworks such as RAGAS and DeepEval
  • Strong coding expertise and a proven problem-solving mindset
  • Flexibility to work effectively in cross-functional engineering environments
  • English proficiency at B2 level or higher

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