Role Overview
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.
Key Responsibilities:
Architecture & Solution Design
- 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)
AI/ML & Data Engineering
- 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, TensorFlow, Scikit-learn)
Generative AI & Agentic Systems
- 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
Cloud & Platform Engineering
- 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
DevOps, MLOps & Governance
- 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
Client Engagement & Leadership
- 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
Technical Skills
Core Architecture
- Deep understanding of:
- Distributed systems
- Microservices and API-first design
- Event-driven architectures
AI / ML / Data
- 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
Programming & Frameworks
- Proficiency in:
- Python (mandatory)
- .NET ecosystem (C#) for enterprise apps
- Experience with:
- FastAPI / Flask
- LangChain / Semantic Kernel / CrewAI
- Familiarity with:
- REST APIs, GraphQL
Cloud & DevOps
- Strong expertise in Azure:
- Azure ML, AKS, Functions, Logic Apps
- Experience with:
- CI/CD pipelines (Azure DevOps / GitHub Actions)
- Docker, Kubernetes
- Knowledge of:
- Infrastructure-as-Code (Bicep, Terraform)
Data & Storage
- Experience with:
- SQL, NoSQL (Cosmos DB, MongoDB)
- Data Lakes and Warehouses
- Understanding of:
- Real-time data processing
Security & Governance
- Knowledge of:
- Identity & Access (Azure AD)
- Data security & encryption
- Responsible AI frameworks
Non-Technical Skills
- Strong consulting mindset with client-facing experience
- Ability to:
- Translate business needs into AI solutions
- Communicate complex AI concepts to non-technical stakeholders
- Proven leadership in:
- Driving architecture decisions
- Managing cross-functional teams
- Excellent:
- Presentation skills
- Documentation and storytelling
Certifications (Preferred)
- Azure Solutions Architect Expert
- Azure AI Engineer Associate
- Azure Data Scientist Associate
- Microsoft Fabric / Data Engineering Certifications
- AWS ML Specialty (optional)
Experience Requirements
- 7+ years in software architecture / enterprise applications
- 3+ years in AI/ML or data-driven solutions
- Proven experience delivering:
- AI-powered enterprise systems
- Cloud-native scalable solutions
Nice to Have
- Experience with:
- Multi-agent AI systems
- Conversational AI (chat + voice + avatars)
- Power Platform + AI integrations
- Teams / Copilot integrations
- Exposure to:
- Enterprise SaaS (multi-tenancy, RBAC, billing)
- Compliance systems (audit logs, SOC2 readiness)
