Description :

Role Overview :

We are seeking an experienced AI Solution Architect to design, lead, and deliver enterprise-scale AI solutions across traditional machine learning, generative AI, and agentic AI systemsprimarily on Microsoft Azure.

This role bridges business strategy, data science, engineering, and architecture, ensuring solutions are scalable, secure, cost-efficient, ethical, and production-ready.

You will own end-to-end AI architecture, including Azure AI Agent Framework, agentic AI patterns, LLM orchestration, MLOps, data pipelines, enterprise integration, security, and governance.

The ideal candidate brings strong architectural depth, hands-on fluency, and the ability to influence technical and business decision-making.

Key Responsibilities :

Architecture & Design :

- Design end-to-end AI/ML architectures (data ingestion ? model development ? deployment ? monitoring).

- Define reference architectures for traditional ML, GenAI (LLMs, RAG, fine-tuning), and agentic AI (multi-agent workflows, function calling, memory, and planning).

- Architect scalable systems using Azure AI Agent Framework, Azure OpenAI, Azure ML, Azure AI Studio, and supporting Azure services.

- Translate business requirements into AI solution designs and architectural blueprints.

- Integrate AI systems with enterprise platforms (ERP, CRM, Data Lakes, APIs, and low-code platforms like Mendix).

- Select models, frameworks, and orchestration strategies optimized for latency, cost, and accuracy.

Agentic & LLM Systems :

- Architect agent-based systems for task planning/decomposition, tool/function calling, multi-agent collaboration, and memory/context management.

- Design RAG pipelines using embeddings, vector DBs, retrieval ranking, and secure ingestion pipelines.

- Define prompt strategies, templates, guardrails, versioning, and context/token governance.

- Implement strategies to reduce hallucination and improve safety, consistency, and explainability.

Data, Integration & MLOps :

- Build AI data pipelines (medallion architecture, feature stores, embeddings pipelines).

- Establish MLOps/LLMOps frameworks for :

- Model lifecycle management.

- CI/CD for AI workloads.

- Monitoring, evaluation, retraining, and drift detection.

- Architect distributed and event-driven integrations using Kafka, Kinesis, or equivalent.

- Ensure data quality, lineage, and governance standards.

Security, Compliance & Responsible AI :

- Define patterns for secure AI deployment, identity/access, and encryption.

- Conduct threat modeling for AI systems and address architecture risks and technical debt.

- Ensure compliance with regulatory standards (SOC2, ISO27001, GDPR, PII, data residency).

- Apply Responsible AI and ethical design guidelines.

- Design Digital Twins integration patterns where applicable.

Leadership & Strategy :

- Own the AI use case backlog, prioritization, and benefits realization in consultation with the CIO.

- Guide enterprise-wide AI strategy, roadmap, and architecture evolution.

- Conduct architecture reviews, solution walkthroughs, and technical assessments.

- Mentor engineering, data, and product teams on emerging AI patterns.

- Impart knowledge to build an internal community of AI Evangelists, including training and competency development.

Required Skills & Qualifications :

- 8+ years in software/data/AI engineering roles.

- Strong grounding in ML/AI fundamentals (supervised/unsupervised learning, NLP, CV, deep learning).

- Hands-on experience with Azure AI platforms :

- Azure OpenAI.

- Azure AI Agent Framework.

- Azure Machine Learning.

- Azure Cognitive Services.

- Proficiency in Python for AI orchestration and integration.

- Experience with vector DBs, embeddings, event-driven systems, REST APIs, and system integration.

- Knowledge of data architecture, governance, and cloud security.

- Experience with containers, Kubernetes, and distributed systems.

- Excellent communication, technical storytelling, and stakeholder alignment skills.

Nice-to-Have :

- Experience with AWS Bedrock, GCP Vertex AI, LangChain, or similar orchestration frameworks.

- Exposure to low-code/no-code AI integration platforms.

- Experience with regulated industries (finance, healthcare, automotive, manufacturing).

- Optional / Future Tech : Quantum AI and advanced emerging AI technologies.

Benefits :

- Opportunity to shape enterprise AI strategy in a rapidly evolving landscape.

- Work with global clients on transformative AI initiatives.

- Collaborative, innovation-driven work culture with integrity and respect.

- Remote-first flexibility and multicultural client exposure.

- Growth path toward enterprise AI leadership and strategy roles.