Solution Architect I

Job Summary

Responsibilities: Architectural Blueprinting: Design scalable and secure data platform blueprints (e.g.,
Lakehouse, Data Mesh, or Data Fabric) that support diverse AI workloads, including generative AI and classical
machine learning. Building scalable, cloud-native storage and processing frameworks (data lakes, lakehouses)
capable of handling massive datasets for model training AI Data Infrastructure Design: Develop specific
architectures for AI-driven workflows, including feature stores, real-time data streaming (Kafka/Spark), and
automated machine learning pipelines. Data Lifecycle Management: Oversee the end-to-end data lifecycle, from
high-fidelity data acquisition and cleaning to preprocessing and model serving. Data Pipeline Automation: Creating
end-to-end automated pipelines for data ingestion, cleaning, and feature engineering to reduce the time from data
raw state to ML model input. Architecting systems that support streaming data (e.g., Kafka, Kinesis) for low-latency
inference in applications like IoT, fraud detection, and customer experience Implementing strict governance,
including metadata management, data lineage (tracking data origin), and quality monitoring to ensure "clean"
data, preventing model failure. Governance & Ethics: Establish unified data governance frameworks that ensure
security, privacy (GDPR/CCPA), and compliance while mitigating algorithmic bias. Stakeholder Collaboration: Act
as the technical bridge between business leadership, data science teams, and IT infrastructure to align technology
with strategic AI objectives. Security & Compliance: Embedding zero-trust principles, role-based access control
(RBAC), and regulatory compliance (GDPR, HIPAA) directly into the data architecture. MLOps
Collaboration: Working closely with data scientists and MLOps teams to integrate feature stores, model registries,
and monitoring tools for continuous retraining Qualifications & Experience Bachelor’s or Master’s degree in
Computer Science, Information Systems, Engineering, or a related field. 10–16 years of experience in data
warehouse /Bigdata Data platform skills, with at least 3-5 years focused on AI/ML supporting infrastructure. Band
– 4.2 /5.1 Deep expertise in cloud platforms like AWS, Azure, or Google Cloud, and big data technologies such as
Apache Spark, ADF, Databricks, and Snowflake. Experience with data governance, security, and compliance
standards. Excellent communication and stakeholder management skills. Keywords – Focus on strategy,
blueprinting, and high-level integration. Architect in Data platform, Vector Databases(pinecone, PGvector, Oracle
Vector DB etc.) , Data Lake houses (data brick, snowflake etc) & Knowledge Graphs, Data Mesh, Data
Fabric, Lakehouse Architecture, Hub-and-Spoke, Lambda/Kappa

Key Responsibilities

To provide an overview on high level implementation roadmaps for the proposed solutions.
2. To develop platform specific architecture solutions.
3. To act as an SME in guiding the team in delivering high quality delivery solutions adhering to client
requirements/policies.
4. To effectively respond to RFPs.
5. To provide cost and pricing data, recovery principles, patterns and usage.
6. To effectively translate client requirements into technical solutions
7. To identity new opportunities for PaaS/SaaS Solutions across the cloud service providers space
Skill

Skill Requirements

Other Requirements