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AI/ML Architect

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Onsite

AI/ML Architect

Tata Consultancy Services

AI/ML Architect

Skills

Google Cloud PlatformSQL QueriesData EngineeringDeep LearningDockerPythonPyTorchTensorFlowDatabricksMachine LearningAIAWSKubernetesPySparkComplianceAzure

19 days ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

4 October 2026

What they need


Role: AI/ML Lead / AI Architect
Experience: 10-15 Years
Location: PAN India

Job Summary-

We are looking for an experienced AI/ML Lead or AI Architect to lead the design, development, and deployment of enterprise AI and Machine Learning solutions. The ideal candidate should possess expertise in AI/ML architectures, Generative AI, cloud platforms, MLOps, and data engineering while providing technical leadership to delivery teams and stakeholders.

Roles & Responsibilities-

  • Define AI/ML strategy, architecture, and roadmap for enterprise solutions.
  • Design and implement Machine Learning, Deep Learning, and Generative AI solutions.
  • Lead end-to-end AI solution development from data ingestion to production deployment.
  • Architect scalable AI platforms using Azure, AWS, or GCP.
  • Implement MLOps frameworks for model training, deployment, monitoring, and governance.
  • Collaborate with business teams to identify AI use cases and translate them into technical solutions.
  • Ensure AI security, compliance, responsible AI practices, and model governance.
  • Mentor AI engineers, data scientists, and development teams.

Mandatory Skills

  • Python
  • Machine Learning & Deep Learning
  • Generative AI & LLMs (GPT, Claude, Llama)
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • Vector Databases (Pinecone, FAISS, Azure AI Search)
  • LangChain / LlamaIndex
  • MLOps (MLflow, Kubeflow, Azure DevOps)
  • SQL & NoSQL Databases
  • Cloud Platform (Azure/AWS/GCP)
  • Docker & Kubernetes
  • Data Engineering Concepts

Preferred Skills

  • Azure OpenAI / AWS Bedrock / Vertex AI
  • Databricks
  • PySpark
  • TensorFlow / PyTorch
  • Microsoft Fabric
  • Agentic AI Frameworks
  • Knowledge Graphs
  • Semantic Kernel

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