AI Data Platform Engineer / Architect

HCLTech; Dadri; On-site; INR 3,800,000 - 6,000,000; Full time

Job summary

HCLTech is seeking a Data Platform Architect with an AI-first mindset to design and lead a modern data architecture across data lakes, feature stores, and real-time pipelines. You will empower data scientists and AI engineers to move from experimentation to production, implementing scalable, secure blueprints and governance.

The role involves crafting lakehouse/data mesh blueprints, cloud-native storage, real-time streaming, and automated ML pipelines, while ensuring data quality, privacy, and

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field.
• 8–15 years of experience in data warehouse/Bigdata Data platform skills with 3–5 years focused on AI/ML supporting infrastructure.
• 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.

Responsibilities

• Architect scalable and secure data platform blueprints (Lakehouse/Data Mesh/Data Fabric) for diverse AI workloads.
• Build cloud-native storage/processing frameworks (data lakes/lakehouses) for large datasets and model training.
• Design AI data infrastructure for feature stores, real-time streaming (Kafka/Spark), and automated ML pipelines.
• Oversee end-to-end data lifecycle from acquisition and cleaning to preprocessing and model serving.
• Create end-to-end automated pipelines for data ingestion, cleaning, and feature engineering.
• Architect systems with streaming data (Kafka/Kinesis) for low-latency inference in IoT, fraud detection, and CX.
• Implement governance with metadata, data lineage, and quality monitoring to ensure clean data.
• Establish unified data governance ensuring security, privacy (GDPR/CCPA), and compliance while mitigating bias.
• Collaborate with business, data science, and IT to align tech with AI objectives.
• Embed zero-trust RBAC and regulatory compliance into the data architecture.
• Work with MLOps to integrate feature stores, model registries, and monitoring for retraining.

Skills

Data platform architecture

AI/ML infrastructure

Cloud platforms

Data governance & security

Stakeholder management

Data processing pipelines

Education

Bachelor’s or Master’s in CS/IS/Engineering

Tools

Apache Spark

Databricks

Snowflake

Kafka

Kinesis

Job description

Role

We are seeking a Data Platform Architect with an AI-first mindset to design and lead the implementation of a modern, enterprise-grade data architecture. You will be responsible for building the technical infrastructure—spanning data lakes, feature stores, and real-time pipelines that enables our data scientists and AI engineers to move from experimentation to high-impact production environments.

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

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field.
• 8–15 years of experience in data warehouse /Bigdata Data platform skills, with at least 3-5 years focused on AI/ML supporting infrastructure.
• 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.

Required 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 Architecture.

Preferred Skills

• Ability to translate high-level business goals into specific technical blueprints.
• Skilled at identifying and resolving bottlenecks in high-volume, low-latency AI environments.