Senior Data & AI Platform Enginee

Job Description
Randstad is seeking a Senior Data & AI Platform Engineer for an onsite role in Washington, DC, supporting a premier national transportation leader. This pivotal role is designed for a technical visionary who will architect and scale a unified, self-service data ecosystem using a modern Databricks lakehouse architecture. You will bridge the gap between complex data engineering and advanced AI, leading the design of automated pipelines, MLOps frameworks, and API-first integrations while mentoring a talented team of engineers. This is a unique opportunity to modernize critical infrastructure and drive data-driven innovation for an organization that connects communities across America.

NOTE :  We are looking for local candidates in Washington, DC who are available to work on a W2 basis and can attend onsite/hybrid work as required.

Key Responsibilities

• Architectural Leadership: Lead the evolution of a scalable Data & AI platform, integrating Databricks, SAP (Datasphere/S/4), and Denodo virtualization into a governed, self-service ecosystem.
• Solution Delivery: Act as a hands-on lead developer for complex data pipelines, feature stores, and API-driven integrations that power enterprise-wide analytics and digital experiences.
• MLOps & Automation: Design and implement production-grade MLOps pipelines, including versioning, CI/CD, and monitoring to accelerate the deployment of intelligent models.
• Engineering Excellence: Establish and enforce standards for ingestion, transformation, and "governance-as-code" controls embedded directly into technical workflows.
• Mentorship: Foster a culture of excellence by providing technical guidance, code reviews, and professional development for junior and mid-level engineers.
• Strategic Collaboration: Partner with architects, product owners, and governance leads to align technical solutions with the broader enterprise data roadmap.

Qualifications & Skills

• Education: Bachelor’s degree in Computer Science, Data Engineering, or a related technical field (equivalent experience considered).
• Experience: 4–6 years of deep technical experience in data engineering, software development, or data architecture.
• Technical Proficiency: Advanced expertise in Python and SQL with significant experience in Apache Spark and modern Lakehouse architectures (Databricks preferred).
• AI/ML Expertise: Proven experience building MLOps pipelines and internal platform services such as feature stores or semantic layers.
• Modern Infrastructure: Strong understanding of API-first and event-driven architectures, secure service-to-service communication, and RBAC security.
• Agile Leadership: Demonstrated ability to lead multi-functional teams through technical challenges within a scaled Agile environment.
• Soft Skills: Exceptional communication and problem-solving abilities, with the capacity to explain complex technical concepts to diverse stakeholders.