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Generative Ai Architect

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Hybrid

Generative Ai Architect

Tredence

Generative Ai Architect

Skills

Google Cloud PlatformData EngineeringDockerPythonData ScienceMicroservicesPyTorchTensorFlowGraphQLMachine LearningAIAWSKubernetesComplianceAzure

2 months ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Hybrid

Published on:

1 September 2026

What they need


We are seeking a highly experienced Generative AI Architect to lead the design, architecture, and solutioning of enterprise-grade GenAI platforms and applications. The ideal candidate will have deep expertise in AI/ML, LLMs, prompt engineering, vector databases, and cloud-native architectures, with proven experience delivering scalable AI solutions across complex environments.

Key Responsibilities

1. Architecture & Solution Design

  • Architect end-to-end GenAI solutions, including data pipelines, model selection, LLM fine-tuning, RAG frameworks, orchestration, and deployment.
  • Design scalable, secure, and cost-optimized GenAI reference architectures on Azure/AWS/GCP.
  • Define solution blueprints integrating LLMs, vector DBs, data lakes, enterprise APIs, and microservices.
  • Evaluate and select appropriate foundation models (OpenAI, Anthropic, Meta, Google, etc.) based on business needs.

2. Technical Leadership

  • Provide architectural governance and best practices across multiple GenAI initiatives.
  • Guide engineering teams on model integration, optimization, evaluation, and MLOps/LLMOps.
  • Drive PoCs, feasibility studies, and technology assessments.

3. RAG & LLM Engineering

  • Architect Retrieval-Augmented Generation (RAG) systems using vector stores (Pinecone, FAISS, Weaviate, etc.).
  • Optimize prompt engineering, model evaluation, grounding, hallucination reduction, and response accuracy.
  • Implement model fine-tuning, domain adaptation, and performance monitoring.

4. Data Engineering & Security

  • Design data ingestion, governance, and preprocessing pipelines for structured, unstructured, and multimodal data.
  • Ensure compliance with enterprise security, privacy, model safety, and responsible AI frameworks.
  • Collaborate with data engineering teams to integrate with enterprise data platforms.

5. Stakeholder Engagement & Delivery

  • Work with business leaders to identify GenAI use cases, define roadmaps, and build ROI-driven solutions.
  • Prepare and deliver solution proposals, architecture documents, technical presentations, and cost estimates.
  • Collaborate with product, engineering, data science, and cloud teams for end-to-end delivery.

Required Skills & Experience

Core Technical Skills

  • 8-14 years of experience in AI/ML, cloud architecture, or enterprise software engineering.

  • 35+ years hands-on experience in LLMs / GenAI platforms.

  • Strong expertise in:

  • LLMs (OpenAI, GPT, Claude, Gemini, Llama, etc.)

  • GenAI frameworks: LangChain, LlamaIndex, HuggingFace

  • RAG architecture, vector databases, embeddings

  • Cloud platforms: AWS (Bedrock/SageMaker), Azure (OpenAI/ML), GCP (Vertex AI)

  • Python, PyTorch, TensorFlow, REST, GraphQL

  • CI/CD, MLOps/LLMOps, containerization (Docker, Kubernetes)

Architecture & Solutioning Skills

  • Enterprise architecture (TOGAF is a plus).
  • Building scalable, distributed microservices.
  • Design of secure AI systems with RBAC, encryption, data masking, model governance.

Soft Skills

  • Strong communication and executive-level presentation skills.
  • Ability to translate complex technical concepts into business-friendly language.
  • Proven leadership in multi-functional teams.

Preferred Qualifications

  • Master’s degree in Computer Science, Engineering, AI/ML, or related field.
  • Certification in cloud architecture (AWS/Azure/GCP).
  • Experience in multimodal AI, agents, or advanced model fine-tuning.
  • Prior experience as an AI architect or lead ML engineer.

Why Join Us

  • Lead strategic AI transformation initiatives for global clients.
  • Work with cutting-edge GenAI tools, models, and frameworks.
  • Opportunity to influence AI strategy and enterprise roadmaps.

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