Teams at Perficient

Designs and implements AWS cloud-native applications, distributed systems, and machine learning infrastructure. Leads architecture for SageMaker model deployment, inference, endpoints, and AI integrations while guiding engineering teams. Builds scalable event-driven services using AWS Lambda, SNS/SQS, EventBridge, DynamoDB, and Bedrock. Develops Python automation and ML integrations, applies Terraform and CI/CD, and ensures production ML workloads are secure, reliable, scalable, and cost-effective. Preferred experience includes NLP, embeddings, generative AI, OCR, MLOps, RAG, and vector databases.

Leads enterprise data onboarding, integration, migration, remediation, and readiness initiatives. Coordinates data engineers, analysts, architects, and business stakeholders across concurrent workstreams. Oversees ingestion, transformation, validation, reconciliation, data quality, governance, and root-cause remediation. Partners with technical teams on scalable Snowflake and cloud data solutions, manages project plans and risks, facilitates Agile ceremonies, and reports delivery status to leadership.

Design, build, and optimize large-scale data pipelines using Databricks, PySpark, ETL/ELT, and lakehouse architectures. Ensure data quality, reliability, performance, and observability while applying Spark optimization techniques. Collaborate with architects, analysts, and stakeholders to deliver scalable data solutions. Implement CI/CD, version control, workflow orchestration, and automation using tools such as Azure Data Factory, Airflow, or Fabric. Communicate technical solutions effectively across technical and non-technical teams.