Sr AI Engineer (Emerging AI)

DAT•Seattle, WA

9d•Hybrid

About The Position

DAT Freight & Analytics is seeking a Sr AI Engineer to build and own the platform powering their agentic solutions. This role involves developing systems that reduce uncertainty in freight logistics through models for rate forecasts, load-to-truck matching, document extraction, fraud signals, and agentic workflows. The engineer will contribute to building the platform from the model gateway and inference layer through feature and vector storage, evaluation harnesses, production observability, and an agentic framework, all on a secure access and utilization spine. This is a high-visibility position with the opportunity to impact all AI teams within the company.

Requirements

10+ years of experience in software engineering, with significant time as a senior or staff engineer owning production infrastructure.
4+ years building and operating machine learning or AI platforms, with experience taking models from notebook to production.
Strong development language skills in TypeScript/js, Java, or Go.
Extensive AWS experience with production systems, including EKS/Kubernetes, Lambda, Redpanda, MSK/Kafka, S3, and Secrets Manager.
Experience with infrastructure-as-code using Pulumi, Terraform, or similar.
Hands-on experience serving models at scale, including containerized inference, GPU scheduling, autoscaling, and batching.
Practical experience with LLM systems in production, including retrieval-augmented generation, vector databases, prompt and context management, tool-calling or agent frameworks, and managed model APIs like Amazon Bedrock.
Proven experience building evaluation and monitoring for models, including offline eval sets, online metrics, drift and data quality checks.
Strong background in event-driven and distributed systems using Redpanda, AWS MSK, or Kafka.
Fluency with observability and on-call health, including designing dashboards, APM traces, logs, alerts, and defining SLOs.
Experience establishing and raising engineering standards for a team (e.g., PR guidelines, deployment validation).
Demonstrated technical leadership, including leading cross-team designs and writing system design documents.
Proven mentoring track record, especially in system design, observability, and operational excellence.
Excellent communication skills, able to explain trade-offs to technical and non-technical stakeholders.

Nice To Haves

Experience with model fine-tuning, distillation, or parameter-efficient training.
Background in marketplace, pricing, forecasting, or document extraction problems.
Freight, logistics, or supply chain domain experience.

Responsibilities

Design, build, and operate shared services for shipping models, including a model gateway for LLM access, inference endpoints, feature storage, vector search, and a common SDK.
Lead architecture for large-scale AI systems, write design documents, and drive alignment across Product, Engineering, and Business users.
Architect and run scalable, reliable AI infrastructure on AWS, utilizing services like Bedrock, SageMaker, EKS, Redpanda, MSK (Kafka), Lambda, and S3, defined in Pulumi.
Build offline and online evaluation systems for model and prompt changes, including regression suites, LLM-as-judge pipelines, A/B and shadow testing, and drift detection.
Own inference cost and latency as key metrics, right-sizing capacity, tuning performance, and providing visibility into workload costs.
Implement guardrails, prompt and output logging, PII handling, access controls, and model/dataset lineage for AI systems.
Drive the adoption of modern software development practices for AI work, including automated testing, code reviews, CI/CD pipelines, and infrastructure-as-code.
Mentor engineers and data scientists on production ML and distributed systems.
Lead incident response and resolution for AI services, perform root cause analysis, and implement preventative measures.