Position Details: Solution Architect - AI Data Engineering

Role: Solution Architect - AI Data Engineering

Location: NJ, GA, IL or VA  

Job Description:

The AI – Data Engineering Architect in TCS’s Americas region is a senior, client-facing role responsible for designing robust, scalable, and secure data architectures that support advanced AI and analytics solutions. This role ensures that enterprise data ecosystems are AI-ready—supporting use cases like retrieval-augmented generation (RAG), multi-agent systems, and real-time inference. You will work across industries (BFSI, Manufacturing, Life Sciences, Telecom, Retail, etc.) to assess current data infrastructure, define end-to-end data pipelines, and architect storage, processing, and integration layers that enable AI models to consume high-quality, timely, and relevant data.

Key Responsibilities:

•Data Architecture Design: Lead the design of conceptual and logical data architectures for AI pipelines, including ingestion, storage, processing, and access layers.

•Enterprise Data Assessment: Evaluate client data ecosystems for AI readiness and recommend modernization strategies.

•Pipeline & ETL Strategy: Define batch, real-time, and streaming data strategies using tools like Spark, Kafka, and cloud-native services.

•Data Storage & Modeling: Architect data lakes, warehouses, vector stores, and feature stores optimized for AI workloads.

•Integration of Heterogeneous Data: Design solutions to combine structured and unstructured data, ensuring consistency and master data alignment.

•Quality, Governance & Security: Embed data validation, lineage tracking, encryption, and compliance (e.g., GDPR, HIPAA) into architecture.

•Scalability & Performance: Design for high throughput and low latency using distributed computing, caching, and cloud auto-scaling.

•Technology Selection & Blueprinting: Recommend tools/platforms and create detailed architecture blueprints and documentation.

•Collaboration with AI Teams: Align data pipelines with AI model requirements, including RAG, feature engineering, and inference needs.

•Prototyping & Validation: Build proof-of-concept pipelines to validate architecture decisions before full-scale implementation.

•Industry-Specific Solutions: Customize architecture for domain-specific needs (e.g., HL7/FHIR in Healthcare, time-series DBs in Telecom).

•Client Engagement & Thought Leadership: Lead workshops, present data strategies, and advise on governance and operating models.

•Implementation Oversight: Guide and review implementation by engineering teams, ensuring alignment with architectural vision.

•Emerging Trends: Stay current with lakehouse paradigms, data mesh, edge computing, and federated learning patterns.

Qualifications: 

•10+ years of experience in data architecture, with 3–5 years in AI/ML or analytics-focused environments.

•Expertise in data modeling (conceptual, logical, physical), normalization, denormalization, and schema design.

•Deep knowledge of big data ecosystems (Hadoop, Spark, Flink) and ETL/ELT strategies.

•Strong experience with cloud data services (AWS, Azure, GCP) and cloud-native architecture.

•Proficiency in relational (PostgreSQL, Oracle), NoSQL (MongoDB, Cassandra), and analytical databases (Snowflake, Redshift).

•Familiarity with vector databases (Pinecone, FAISS), graph databases (Neo4j), and time-series databases.

•Experience with data integration tools (Informatica, Talend, ADF, Glue) and messaging systems (Kafka, MQ).

•Understanding of RESTful APIs, data services, and microservices for data access.

•Knowledge of data governance, cataloging, lineage, and quality frameworks (e.g., Collibra, Great Expectations).

•Strong grasp of data security, encryption, access control, and compliance standards (GDPR, HIPAA, SOX).

•Excellent communication and stakeholder management skills; ability to lead workshops and present to executives.

•Experience leading technical teams and overseeing architecture implementation.

•Strong analytical and problem-solving skills for diagnosing and resolving data architecture challenges.

•Broad domain knowledge across BFSI, Healthcare, Manufacturing, Retail, and Telecom.

•Familiarity with project management practices and agile delivery.

•Continuous learning mindset with awareness of emerging technologies (e.g., lakehouse, data mesh, edge computing).

•Certifications (nice-to-have): AWS Certified Data Analytics – Specialty, Azure Data Engineer/Architect, Cloudera CCP Data Engineer.

Salary Range: $131,750 - $178,250 a year

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