Solution Architect (Data & AI)

Highlights

Responsibilities

Solution Design & Architecture

• Design end-to-end data and AI architectures across ingestion, transformation, storage, analytics, and ML layers.
• Build scalable, cloud-native ecosystems using Azure, Snowflake, and open-source frameworks (Spark, Airflow, Kafka).
• Define architecture blueprints, data models, and governance-aligned integration patterns.
• Develop AI/ML platforms with MLOps, monitoring, and responsible AI principles.

Technical Leadership

• Lead architecture discussions, design reviews, and guide data/ML teams through delivery.
• Drive modern data engineering practices (CI/CD, IaC, DevOps).
• Evaluate and recommend emerging technologies for enterprise solutions.

Client Consulting & Engagement

• Translate business needs into scalable data-driven solutions.
• Support pre-sales, RFPs, and executive-level presentations.
• Advise on data strategy, architecture roadmaps, and AI adoption frameworks.

Delivery & Governance

• Ensure architectural integrity, scalability, and compliance with data privacy standards.
• Define best practices, reusable assets, and mentoring frameworks for internal teams.

Key Skills Requirements

• Proficient in end-to-end AI architecture (data prep to deployment) with MLOps, Spark, Kafka, Airflow, Hadoop, dbt, and Delta Lake.
• Expertise in LLMs, GenAI models, and AI agents using Azure OpenAI, LangChain, and Semantic Kernel.
• Experience with RAG pipelines, vector databases, and governance frameworks.
• Hands-on expertise in Azure Synapse, Data Factory, Databricks, Purview, and Machine Learning.
• Experience with Azure OpenAI, Cognitive Services, and Microsoft Fabric.
• Efficiency in data lakehouse design, Snowpark, Streamlit, optimization for large-scale analytics, automation using dbt, Airflow, and Terraform.
• Strong programming skills in Python, SQL, and Scala, and experience with API/microservice-based data integration.
• Expertise in CI/CD with Azure DevOps, GitHub Actions, Terraform, Docker, and Kubernetes.
• Proven client-facing and advisory consulting experience with strong analytical, communication, and stakeholder management skills.
• Bachelor’s/Master’s in Computer Science, Data Engineering, or other related fields.
• Minimum 8 years of total experience, with at least 2 years in Data/AI solution architecture roles.
• Preferred certifications: Azure Solutions Architect Expert, Data Engineer Associate, SnowPro Core, Advanced Architect, and TOGAF (optional).