Lead Data Architect

About the role

The Lead Data Architect at Karsun Solutions will design, build, and operate enterprise data platforms that power GenAI and AI/ML use cases. This role is highly technical and hands-on, focusing on data platform architecture, end-to-end data engineering, ML/LLM pipeline design, production model onboarding, and delivery of scalable Databricks-centric solutions across cloud environments.

Responsibilities

• Architect and implement enterprise data platforms (batch + streaming) optimized for ML, LLMs, and GenAI workloads.
• Lead design and hands-on implementation of Databricks workspaces, Unity Catalog, Delta Lake design patterns, cluster policies, and performance tuning.
• Build and own end-to-end data pipelines (ingest, transform, feature engineering, serving) using PySpark, Databricks Jobs, Spark SQL, Delta Lake, and orchestration tools.
• Design and operationalize model training, fine tuning (LLM), evaluation, deployment, and monitoring pipelines (MLOps/RAG/CAG) integrating Databricks MLflow, CI/CD, and infra-as-code.
• Implement vectorless and vectorization/embedding pipelines, vector store integrations, and retrieval layers for RAG (FAISS, Pinecone, Weaviate, Milvus).
• Define data schemas, governance, lineage, access controls, and data product APIs; implement Unity Catalog or equivalent for centralized governance.
• Drive cost/performance optimization for storage, compute (spot/preemptible), and query patterns.
• Collaborate with engineers, data scientists, product owners, and security to translate business needs into production GenAI solutions.
• Mentor and lead engineering teams; conduct architecture reviews, code reviews, and run technical deep dives.
• Create reproducible experiment tracking, model registry, and rollout strategies (canary, shadow testing, rollback).
• Stay current on GenAI/LLM architectures and evaluate/introduce new tooling and frameworks.

Requirements

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology, or a related field.
• 8+ years of hands-on experience in data engineering/platform architecture; 3+ years in an architect or lead role.
• Active AWS Certified Machine Learning – Specialty certification.
• Proven, hands-on Databricks experience (designing workspaces, Delta Lake, performance tuning, productionizing Spark jobs).
• Strong Spark + PySpark expertise and experience with Databricks Runtime.
• Deep experience with ML/LLM pipelines and operationalizing models (training, fine tuning, serving).
• Practical experience with vector embeddings, semantic search, and RAG architectures.
• Solid Python expertise and common ML libraries (PyTorch, TensorFlow, Hugging Face transformers) and MLflow.
• Cloud platform experience (AWS strongly preferred).
• Experience with containerization and orchestration while leveraging open source libraries for unstructured and structured data processing, serving/inference.
• Strong SQL skills; experience with distributed query/warehouse systems and parquet/AVRO/Delta formats.
• Certified in CI/CD and infra-as-code experience (Terraform, GitOps, Jenkins/GitHub Actions/GitLab CI).
• Data governance, security, and IAM experience; experience implementing row/column level access controls and data lineage.
• Demonstrated ability to design for scalability, reliability, and cost efficiency.

Preferred Qualifications

• Prior experience with Databricks Unity Catalog, Photon, and Databricks SQL.
• Experience integrating Databricks with vector databases (Pinecone, neo4j) and retrieval frameworks (LangChain, LlamaIndex).
• Familiarity with AWS Bedrock or other managed LLM services.
• Experience with realtime streaming (Kafka, Kinesis) and stream processing on Databricks Structured Streaming.
• Experience with large-scale ETL frameworks and tools (Airflow, Prefect).