15+ AI Architect at Plano TX (ONSITE)

Location: Plano TX (ONSITE)

Duration: Long Term

Job Description:

• Forward Deployed Engineer — AI Architect
• Project: Data Observability RFQ — DEPOT

Job Summary:

• The Forward Deployed Engineer — AI Architect is a senior-level, full-time DXC employee embedded within the client environment to lead the architectural design and delivery of AI-driven capabilities for the Data Observability RFQ — DEPOT platform.
• This role serves as the primary bridge between the client’s strategic data objectives and the engineering team, translating complex requirements into scalable, production-grade AI solutions.
• The AI Architect owns the end-to-end design of machine learning pipelines, anomaly detection systems, and intelligent data quality frameworks that power the DEPOT observability platform.
• Operating at a senior level, this role combines deep technical authority with hands-on engineering execution and the communication skills to influence stakeholders at all levels.

Responsibilities:

• Serve as the on-site AI architecture lead, partnering directly with client stakeholders to define and deliver AI/ML solutions aligned to DEPOT platform objectives.
• Design and own the architecture of machine learning pipelines, anomaly detection models, and AI-driven data quality monitoring systems within the observability platform.
• Translate client business and data requirements into technical AI specifications, solution blueprints, and implementation roadmaps.
• Collaborate with data engineers, platform engineers, and DevOps/SRE teams to ensure seamless integration of AI components into the broader DEPOT ecosystem.
• Evaluate and recommend AI/ML frameworks, tooling, and cloud services; oversee model selection, validation, and deployment lifecycle management.
• Establish MLOps practices including model versioning, monitoring, retraining pipelines, and drift detection to ensure sustained model performance in production.
• Lead technical design reviews, architecture walkthroughs, and knowledge-transfer sessions with both client and DXC engineering staff.
• Proactively identify risks in AI system design and data quality pipelines; develop mitigation strategies and communicate findings to project leadership.

Required Skills:

• Client-facing AI architecture and data observability: Proven experience engaging directly with enterprise clients to architect AI/ML solutions; deep expertise in data observability platforms, data quality monitoring, anomaly detection, and intelligent pipeline frameworks (e.g., Monte Carlo, Great Expectations, or custom ML-based monitors); strong knowledge of feature engineering, model deployment patterns, and LLM/generative AI integration.
• Senior engineering proficiency: Hands-on experience building and deploying ML pipelines using Python, Spark, or Scala; fluency with cloud ML platforms (AWS SageMaker, Azure ML, or Google Cloud Platform Vertex AI), MLOps tooling (MLflow, Kubeflow, or similar), REST/gRPC APIs, and containerized environments (Docker, Kubernetes); experience with SQL and NoSQL data stores supporting model feature serving.

Preferred Skills:

• Forward-deployed and consulting experience: Background in client-embedded or consulting roles within regulated industries (finance, government, or healthcare); familiarity with enterprise data governance, DataOps practices, and aligning AI solution delivery to contractual or compliance requirements.
• Advanced AI/MLOps and observability tooling: Experience with real-time streaming ML inference (Kafka, Flink), OpenTelemetry for AI system tracing, FinOps cost attribution for ML workloads, and knowledge of emerging AI observability frameworks and responsible AI/fairness monitoring practices.