Technical Architect – AI, ML

A Technical Architect (TA) – AI & ML drives end-to-end design, development, and deployment of intelligent, AI-powered solutions across cloud and hybrid environments. This role combines enterprise application architecture with advanced AI/ML engineering, Generative AI, and agentic systems design
. The TA acts as a customer-facing technical leader, responsible for translating business problems into scalable AI-powered architectures, enabling data-driven decision-making, automation, and intelligent workflows
. This role spans pre-sales, discovery, solution design, build, deployment, and optimization
, with a strong emphasis on Microsoft Azure AI ecosystem
.

• Lead architecture for AI-first enterprise applications
  , including:
  • Machine Learning systems
  • Generative AI (LLMs, copilots, agents)
  • Predictive analytics platforms
• Design scalable, secure, and cost-optimized architectures using Azure services
• Translate business use cases into AI/ML pipelines, data models, and intelligent workflows
• Define multi-agent orchestration architectures (Semantic Kernel / CrewAI / Azure AI Foundry)

• Design and oversee:
  • Model training, evaluation, deployment (MLOps)
  • Data ingestion and transformation pipelines
• Implement:
  • Feature engineering pipelines
  • Model monitoring, drift detection, and retraining strategies
• Guide teams on ML frameworks (PyTorch, Tensor Flow, Scikit-learn)

• Architect enterprise-grade solutions with:
  • Azure OpenAI / GPT models
  • RAG (Retrieval-Augmented Generation)
  • Copilot-style experiences (chat, voice, agents)
• Design:
  • Memory systems (vector DBs, Cosmos DB)
  • AI orchestration workflows
• Build autonomous agent systems for:
  • Reporting
  • Workflow automation
  • Decision augmentation

• Lead deployment of AI solutions on:
  • Azure AI Foundry / Azure ML
  • Azure Functions, AKS, App Services
• Ensure:
  • High availability, scalability, and performance
  • Secure model serving and API layers

• Implement:
  • CI/CD pipelines for AI/ML workloads
  • Model lifecycle management
• Define governance around:
  • AI ethics and responsible AI
  • Data privacy and regulatory compliance (SOC2 readiness, audit logs)
• Enable:
  • Monitoring, observability, anomaly detection

• Lead:
  • Discovery workshops
  • AI solution envisioning sessions
• Present to:
  • C-level executives and stakeholders
• Support:
  • Proposal development, estimations, and technical documentation
• Mentor engineering teams on AI architecture best practices

• Deep understanding of:
  • Distributed systems
  • Microservices and API-first design
  • Event-driven architectures

• Strong experience in:
  • Machine Learning lifecycle (training → deployment → monitoring)
  • Deep learning fundamentals
  • NLP, computer vision (preferred)
• Hands-on with:
  • Azure ML, Azure AI Services, Azure OpenAI
  • Databricks / Synapse / Fabric
• Experience with:
  • Vector databases (Azure AI Search, Pinecone, Redis)
  • RAG architectures

• Proficiency in:
  • Python (mandatory)
  • .NET ecosystem (C#) for enterprise apps
• Experience with:
  • FastAPI / Flask
  • Lang Chain / Semantic Kernel / CrewAI
• Familiarity with:
  • REST APIs, GraphQL

• Strong expertise in Azure:
  • Azure ML, AKS, Functions, Logic Apps
• Experience with:
  • CI/CD pipelines (Azure Dev Ops / Git Hub Actions)
  • Docker, Kubernetes
• Knowledge of:
  • Infrastructure-as-Code (Bicep, Terraform)

• Experience with:
  • SQL, No
    
    SQL (Cosmos DB, MongoDB)
  • Data Lakes and Warehouses
• Understanding of:
  • Real-time data processing

• Knowledge of:
  • Identity & Access (Azure AD)
  • Data security & encryption
  • Responsible AI frameworks

• Strong consulting mindset with client-facing experience
• Ability to:
  • Translate business needs into AI…