Azure AI Application Architect/MLOps Consultant

Key Responsibilities :

- Architect and implement intelligent applications using Azure AI services including Azure OpenAI, Azure Cognitive Services, and Azure Machine Learning.

- Lead AI experimentation initiatives to evaluate models, configurations, and deployment strategies aligned with business goals.

- Embed AI capabilities such as NLP, computer vision, speech recognition, and decision-making into enterprise applications.

- Integrate AI models with internal and external systems (web, mobile, APIs) ensuring seamless interaction with business logic.

- Design and implement scalable, low-latency, and high-availability AI components using Azure App Services, AKS, and serverless patterns.

- Provision and manage Azure AI Foundry workspaces, compute environments, and model endpoints.

- Implement secure access controls, RBAC, and data governance policies to ensure compliance and responsible AI practices.

- Guide development teams on MLOps best practices including model versioning, CI/CD pipelines, monitoring, and drift detection.

- Collaborate with business stakeholders to identify opportunities for AI-led process optimization and transformation.

- Contribute to internal AI strategy committees and innovation forums to shape enterprise AI adoption.

- Document architecture patterns, reusable components, and integration blueprints for AI applications.

- Actively participate in hands-on coding and development of AI solutions.

Required Skills :

Azure AI & ML :

- Azure AI Foundry : Workspace provisioning, model lifecycle management

- Azure OpenAI : Prompt engineering, model fine-tuning, endpoint management

- Azure Cognitive Services : Vision, Speech, Language, and Decision APIs

- Azure Machine Learning : Training, deployment, monitoring, and MLOps

- Experience with LLM orchestration and prompt chaining

- Familiarity with Azure AI Studio and Azure Arc

- Proficiency in using Azure AI SDKs within custom applications

- Hands-on experience in at least one AI/ML domain (Computer Vision, Automation, Predictive Analytics, RPA, etc.) with relevant

libraries (Pandas, TensorFlow, Scikit-learn, etc.)

Application Integration :

- Programming : Python, .NET, JavaScript/TypeScript

- Integration : REST APIs, Azure Functions, Logic Apps, Event Grid

- Architecture : Microservices, serverless, event-driven, real-time & batch inference pipelines

- Infrastructure : Azure App Services, AKS, API Management

- Experience with containerized AI workloads (Docker, Kubernetes)

Security & Governance :

- RBAC, Managed Identities, Private Endpoints

- Data privacy, encryption, and responsible AI principles

Preferred Skills :

- Exposure to multi-cloud AI deployment strategies.

- Knowledge of AI-driven business process automation and Agentic AI frameworks.

Soft Skills :

- Strong analytical and problem-solving mindset.

- Proactive, self-managed, and results-oriented.

- Excellent communication and stakeholder engagement skills.

- Team player with mentoring and leadership capabilities.

- Curious, innovative, and eager to learn and share knowledge.

- Strong project tracking and execution discipline.

Qualifications :

- Bachelors or Masters degree in Computer Science, IT, or Data Science.

- Preferred : Engineering/Science graduate with specialization in AI/ML.

- Mandatory Certification : Microsoft Certified : Azure AI Engineer Associate and/or Azure Solutions Architect Expert.

Deliverables :

- Enterprise-grade intelligent applications powered by Azure AI.

- Architecture patterns, reusable AI components, and integration blueprints.

- Documented best practices for MLOps and AI governance.

- AI-led process transformation roadmaps and strategy recommendations.

Interview Process :

- Scenario-based technical discussions.

- Hands-on assignment (to be submitted within the given timeline).