Applied AI Engineer

Sourced from apply.workable.com

Foresight is building a control tower for large-scale capital projects, connecting schedules, field progress, and execution data to forecast delivery risk. As an Applied AI Engineer, you'll architect, train, and deploy the core ML models behind risk prediction, criticality prediction, project planning, and progress tracking. This is a hands-on role owning models and backend features from training through deployment and monitoring, working with complex multimodal data.

• Own ML features from technical design through release, measuring adoption and iterating
• Build, train, and deploy models for risk prediction, criticality prediction, project planning, and progress tracking
• Work with full-stack engineers to turn models into production features
• Tune performance across the modeling stack for accuracy, latency, and reliability
• Raise the bar on architecture, testing, and observability
• Partner with product, design, and customers to identify high-impact problems
• Help shape the product and technical roadmap

• 3+ years building and shipping ML models in production with backend ownership
• 1+ years building and shipping VLM/LLM-based features to production
• Strong Python backend skills (FastAPI, Django, or similar)
• Solid relational data modeling in Postgres (schema design, indexes, query plans, migrations, ORM such as SQLAlchemy)
• Experience shipping agentic workflows, RAG systems, and high-volume data pipelines
• Experience with async work: queues, background jobs, retries, idempotency
• Hands-on with AWS (S3, RDS, managed queues, containers or serverless)
• Strong testing and production debugging from logs, metrics, and traces
• Clear written communication and pragmatic trade-off making

• Workflow orchestration for multi-step jobs (Temporal, Step Functions, Airflow)
• Legacy-to-modern migration experience
• Experience with regulated enterprise or public-sector customers (SOC 2, data residency)
• Early-stage or founding-team experience