Job Description

AI Architect

A highly experienced AI Architect with 10–12 years of professional experience in designing, building, and scaling enterprise-grade AI and Generative AI (GenAI) solutions across leading public cloud platforms (Azure, AWS, GCP). The ideal candidate will play a strategic role in AI solution architecture, data & model engineering, cloud integration, and MLOps/LLMOps frameworks, while working closely with business and technology teams to drive measurable outcomes through AI innovation.

Key Responsibilities

• AI Solution Architecture:

• Enterprise Integration:

• GenAI Solution Design:

• MLOps & LLMOps:

• Data Architecture Collaboration:

• Stakeholder Engagement:

• Innovation & Research:

• Performance Optimization:

• Mentorship:

Technical Skills & Competencies

• AI/ML Frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face, Lang Chain, LlamaIndex, Open AI API, etc.
• GenAI & LLMs: Experience with GPT-4/4o, Claude, Gemini, Mistral, LLaMA, fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering.
• Cloud Platforms: Deep understanding of Azure AI (OpenAI, AI Search, ML), AWS (SageMaker, Bedrock, Comprehend, Lex), GCP Vertex AI.
• MLOps & LLMOps: Azure ML Pipelines, Sage Maker Pipelines, MLflow, Kubeflow, or custom CI/CD for AI.
• Data & Integration: Experience with Data Lakes, Databricks, Synapse, Kafka, REST/GraphQL APIs, and vector DBs like Pinecone, We aviate, FAISS, Milvus, or Cosmos DB.
• Programming: Python, SQL, REST APIs, YAML, Docker, Kubernetes, Terraform (IaC).
• Responsible AI: Understanding of bias detection, model explain ability, and governance principles.
• Architecture Design: Expertise in TOGAF/Well-Architected frameworks and AI reference architectures.

Qualifications

• Proven experience in designing and delivering AI solutions at enterprise scale.
• Cloud certifications (Azure AI Engineer, AWS ML Specialty, or GCP ML Engineer) preferred.
• Strong communication and presentation skills for stakeholder engagement and executive alignment.

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