Lead Data Architect

Why Karsun? Join Karsun Solutions to grow your career with the company transforming possible for the US Government. At Karsun, collaboration drives our community. We're committed to building an environment where team members from diverse backgrounds can innovate, learn and grow with us. Here at Karsun, the only limit to your potential is the limit of your curiosity. Join Team Karsun, and Find Your Next! Summary The Lead Data Architect will design, build, and operate enterprise data platforms that power GenAI and AI/ML use cases. This is a highly technical, hands-on role responsible for 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 . What You'll Be Doing: - 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. - Implement observability for data and ML pipelines (metrics, logging, data quality tests, alerting). - 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. Required Qualifications: BA or BS degree in CS, Computer Engineering, Information Technology or a related field. 8+ years hands on experience in data engineering/platform architecture; 3+ years in an architect or lead role. Candidate must hold an active AWS Certified Machine Learning - Specialty certification or equivalent AWS certification. Proven, hands on Databricks experience (designing workspaces, Delta Lake, performance tuning, productionizing Spark jobs). Deep Spark + PySpark expertise and experience with Databricks Runtime. Strong experience building 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. CI/CD and infra-as-code experience (Terraform, GitOps, Je Karsun Solutions