Data Platform Architect / Senior Data Engineer

We are seeking a highly experienced Platform Architect to design and build the next generation of cloud-native, scalable, and LLM-enabled data platforms. This role requires deep expertise across modern data architectures, semantic layers, governance frameworks, and infrastructure patterns that support enterprise-grade AI and data products. The architect will play a pivotal role in defining platform standards, enabling data interoperability, and ensuring AI systems operate efficiently across large-scale environments.

Key Responsibilities:

Architecture & Platform Design

· Architect scalable, cloud-native data platforms (AWS/Azure/Google Cloud Platform) to support AI/ML, analytics, and high-throughput data applications.

· Design LLM-enabled infrastructure, including model-serving layers, vector databases, RAG components, and integration with semantic data layers.

· Build foundational components such as:

·        Distributed compute environments (Spark, Databricks, Ray)

·        Feature stores

·        Vector search systems

·        Metadata, lineage, and governance frameworks

· Define target architecture, reference patterns, and platform standards to accelerate internal teams.

Semantic Layer & Data Modeling

· Implement modern semantic modeling approaches (dbt semantic layer, LookML, or custom frameworks).

· Build semantic knowledge layers enabling unified metrics, discoverability, and contextual understanding for LLM applications.

· Partner with ML/NLP teams to align semantic representations with retrieval, embedding, or knowledge graph layers.

· Ensure consistent data definitions, canonical models, and enterprise-wide data abstractions.

Data Governance, Observability & Reliability

· Define and enforce policies for data quality, lineage, access controls, cataloging, privacy, and compliance.

· Implement governance tools such as Amundsen, DataHub, Collibra, or Atlas.

· Establish SLA/SLO frameworks for platform reliability, cost control, and operational transparency.

· Build monitoring solutions for pipelines, model performance, drift, data freshness, and API health.

AI / ML Platform Integration

· Architect interfaces and pipelines that support:

·        Model training and retraining

·        Batch and streaming data ingestion

·        Feature computation and storage

·        Model orchestration workflows

· Enable LLM-based systems such as:

·        RAG frameworks

·        Prompt routing and orchestration

·        Fine-tuning pipelines

·        Enterprise context integration

· Work closely with ML engineers to design GPU/accelerated compute environments and efficient model serving architectures.

Technical Leadership & Collaboration

· Work cross-functionally with data engineering, ML engineering, backend engineering, and product teams.

· Create architecture documentation, RFCs, blueprints, and future-state roadmaps.

· Mentor engineering teams on modern platform engineering best practices.

· Evaluate emerging tools and technologies that enhance platform capabilities.

Required Skills:

- 8–18 years data engineering/platform architecture

- Cloud-native design, distributed systems

- Kafka, Spark, dbt, Airflow, governance tools

Preferred Skills:

- RAG/ML platform integration

- Knowledge Graph familiarity

- Strong engineering fundamentals.

We are proud to be an Equal Employment Opportunity (EEO) and Affirmative Action employer. We at  HL Solutions do not discriminate based on Race, Religion, Color, National origin, Sex, Sexual orientation, Gender identity, Gender expression, Age, and Disability status.