Artificial Intelligence Solution Architect
We are seeking a highly skilled Artificial Intelligence Solution Architect to lead the end-to-end architecture of AI/ML solutions on Azure.
* Main Responsibilities:
* Develop high-level solution designs that integrate with clients' existing data platforms and infrastructure.
* Work closely with enterprise clients to understand business challenges and identify opportunities where AI/ML can drive value.
* Provide hands-on technical leadership to delivery teams, guiding Azure AI Engineers and Data Engineers in implementing best practices for data preparation, model development, and cloud deployment.
* Mentor team members in advanced AI techniques and review designs/code to ensure quality.
* Establish and enforce MLOps best practices for the team, including reproducible workflows, continuous integration/continuous delivery (CI/CD) for ML models, automated testing, and monitoring of model performance in production.
* Stay abreast of the latest AI trends and Azure services, evaluating new technologies – from Azure Cognitive Services and Azure OpenAI to emerging open-source frameworks for LLMs (Large Language Models) and RAG (Retrieval-Augmented Generation).
* Oversee and provide guidance on multiple AI projects in parallel, ensuring architectural consistency and reuse of best practices across engagements.
* Act as the go-to expert for solving complex technical problems and making high-level design decisions.
Requirements
* Experience: 7+ years of experience in data analytics and software development, with at least 4–5 years in designing and implementing ML/AI solutions at scale.
* Azure Expertise: Deep hands-on knowledge of Azure data and AI services – including Azure Machine Learning, Azure Databricks, Azure Data Lake/Synapse, Azure Cognitive Services (Text, Vision, Speech), Azure OpenAI and Azure AI Foundry.
* Architectural Skills: Strong skills in system design and integration. Comfortable defining solution architectures that encompass data ingestion, feature engineering, model training, deployment (APIs, containers), and monitoring.
* MLOps & Software Engineering: Solid understanding of MLOps principles and experience implementing ML lifecycle management (source control, CI/CD for models, model registries, etc.) on Azure or similar platforms.
* Leadership & Communication: Excellent leadership and interpersonal skills. Able to interface with client stakeholders to explain complex AI concepts in business terms, gather requirements, and drive adoption.
* Ai Knowledge: Broad knowledge of machine learning and AI techniques (supervised, unsupervised learning, time-series, etc.). Familiarity with deep learning and NLP. Exposure to generative AI and LLMs is highly desired.