AI Solution Architect - Remote

Title : Solution Architect

Duration – 12+ months

Remote 90%, Travel for some important meetings may be 10%

Look for EST or CST candidates

Location – Washington, D.C (Remote )

Candidates are going to need to have some kind of transportation domain experience whether it is railway, airlines, or transit authorities.

Job Description Is Below. Solutions Architect Sr.

• 10+ years of progressive IT/technology experience with a foundation in hands-on engineering delivery — application development, systems integration, or data platform work — before moving into architecture; candidates who have only held advisory roles will not be competitive.
• 5+ years as a Solutions or Enterprise Architect with demonstrated ownership of complex, multi-system solutioning in rail, transit, airlines, or transportation operations; exposure to scheduling, dispatch, crew management, asset maintenance, and train control function as a connected operational ecosystem is a plus.
• Proven ability to perform structured business process analysis — process mapping, As-Is/To-Be modeling, gap analysis, workflow decomposition — not systems architecture in isolation; the work requires equal fluency in operational process and technical design.
• Deep command of integration architecture: event-driven patterns, REST/GraphQL APIs, pub/sub and streaming (Kafka or equivalent), and data exchange in operational contexts spanning OT/IT boundaries.
• Experience designing for 24/7 operational availability on cloud and hybrid platforms (AWS, Azure, or GCP); familiarity with the reliability, latency, and fault-tolerance requirements of safety-adjacent rail environments.
• Ability to lead technical solutioning for operational data pipelines — real-time streaming ingestion, event processing, and lakehouse patterns applied to train performance, delay causality, and asset health data.
• Demonstrated ability to produce rigorous, execution-grade architecture artifacts: solution design documents, integration maps, API contracts, non-functional requirement matrices, and Architecture Decision Records that engineering teams can build against without interpretation overhead.
• Direct engagement experience with operational stakeholders — dispatchers, controllers, planners, front-line supervisors — to ground architecture decisions in actual workflow context rather than idealized process models.
• Familiarity with AI/ML-enabled operations: predictive maintenance, anomaly detection, scheduling optimization, or computer vision inspection pipelines; experience with lakehouse architecture (Databricks, Delta Lake, Unity Catalog) applied to operational and engineering data is a strong differentiator.
• Prior contractor or consulting engagement model experience; able to deliver independently, manage scope, communicate proactively on blockers, and maintain artifact quality without close supervision — including support for RFP/RFQ technical inputs, vendor evaluations, and architecture governance participation.