Solution Architect I

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, To provide an overview on high level implementation roadmaps for the proposed solutions.

1. To develop platform specific architecture solutions.
2. To act as an SME in guiding the team in delivering high quality delivery solutions adhering to client

requirements/policies.

1. To effectively respond to RFPs.
2. To provide cost and pricing data, recovery principles, patterns and usage.
3. To effectively translate client requirements into technical solutions
4. To identity new opportunities for PaaS/SaaS Solutions across the cloud service providers space

Architectural Services, Blueprints, Information/Data Security (InfoSec), Artificial Intelligence (AI), Machine Learning, Scalable System Development, Cloud Storage, Data Processing, Training Data Sets, Data Modeling, 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

Information at a Glance

Why HCLTech?

At HCLTech, youll supercharge your potential. Youll find your career. And youll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first.

HCLTech is a global technology company, home to more than 223,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending June 2026 totaled $14.8 billion.