Artificial Intelligence Engineer

Senior ML / Applied AI Engineer

Location: Remote (EST hours)

Type: Contract

Team: Applied ML / Data Platform

We’re looking for a Senior ML / Applied AI Engineer to join us in a high-impact, time-bound engagement as a subject matter expert (SME). You’ll embed with our internal engineering team to accelerate MLOps and applied ML platform capabilities
, co-build core frameworks, establish standards, and enable long-term ownership through strong knowledge transfer.

This role is ideal for someone who thrives in hands-on platform engineering
, enjoys shaping end-to-end ML systems
, and has real-world experience supporting production LLM use cases
.

You’ll help accelerate and mature capabilities across:

• ML workflow and environment design
• Feature pipelines and training architecture
• Model deployment and lifecycle management
• Observability, evaluation, and governance patterns

• Partner closely with internal engineers to design and implement scalable ML workflows across development, training, and production
• Build and enhance feature pipelines and training orchestration to support reproducibility and fast iteration
• Establish best practices for model deployment, versioning, promotion, and rollback
• Implement lifecycle management patterns for:
• Training datasets and feature versions
• Model artifacts and metadata
• Inference endpoints and evaluation outputs
• Design and operationalize LLM infrastructure for both current production needs and rapidly growing demand
• Build frameworks and tooling to effectively harness LLMs, including:
• Prompt and version management
• Retrieval workflows (where applicable)
• Evaluation harnesses and quality scoring
• Latency and cost monitoring and optimization
• Define and implement observability and governance standards (monitoring, drift detection, evaluation, auditability)
• Collaborate with data pipeline engineers to ensure clean platform alignment and handoffs
• Document patterns and deliver clear knowledge transfer
  , enabling the internal team to maintain and extend the platform independently

• A repeatable framework for training → deployment → monitoring → iteration
• Faster launch of LLM use cases through shared infrastructure and standards
• Standardized evaluation, observability, and governance practices
• Internal engineers fully enabled through documentation, examples, and knowledge sharing
• A reliable, traceable, end-to-end model lifecycle in production

• 6+ years of experience in Applied ML Engineering, MLOps, or ML Platform Engineering
• Strong understanding of ML model lifecycle management (data → training → deployment → monitoring)
• Experience designing training and feature pipeline architectures
• Proven ability to build production-grade ML systems
  , including:
• Model packaging and deployment
• Versioning and artifact management
• Evaluation and monitoring
• Hands-on experience with LLMs in production (inference patterns, evaluation, cost/latency tradeoffs)
• Strong software engineering skills (
  Python preferred
  ) with clean coding, testing, and documentation practices
• Comfortable working in an embedded, collaborative development model (PRs, shared repos, code reviews)

• Experience with ML orchestration and workflow tools (
  Airflow, Prefect, Dagster, Kubeflow
  )
• Familiarity with experiment tracking and model registries (
  MLflow or similar
  )
• Experience with containerized deployment and cloud-based ML environments
• Strong perspective on LLM evaluation, governance, and operational safety
• Background building internal ML platforms or developer tooling