> Markdown version of [/jobs/ext/3975661-solution-architect-i](https://www.wearedevelopers.com/jobs/ext/3975661-solution-architect-i). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Solution Architect I

- **Company:** HCL Technologies
- **Location:** Plymouth, MN, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Software as a Service, Cloud Storage, Encodings, Cyber Security, Information Systems, Data Architecture, Data Governance, Data Infrastructure, Data Security, Data Warehousing, Fraud Prevention and Detection, Graph Database, Monitoring of Systems, Information Lifecycle Management, Machine Learning, Meta-Data Management, Oracle (Applications), Platform as a Service (PAAS), Systems Development Life Cycle, Role-Based Access Control, Cloud Services, Data Mesh, Zero Trust Network Access, Data Streaming, Pinecone, Feature Store, Data Processing, Google Cloud, Feature Engineering, Data Ingestion, Snowflake, Apache Spark, Microsoft Fabric, Pgvector, Data Lakes, Information Technology, Data Lineage, Low Latency, Apache Kafka, Machine Learning Operations, Stream Processing, Data Pipelines, Databricks
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/solution-architect-i-plymouth-mn--f1c99151-eb9e-4044-a015-847d0afe164f

## About the Role

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

## Description

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.

 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