Applied AI Engineer

Foresight Works Ltd•New York, NY

7h•$180,000 - $230,000

About The Position

Foresight is hiring an Applied AI Engineer to build the core machine-learning engine of its platform for delivering complex capital projects. The role involves architecting, training, and deploying models for risk predictions and AI recommendations, and bringing those models into interfaces that make large-scale projects with as many as 100,000 activities understandable. This is a hands-on position on a new team, with ownership of models and backend features from training through deployment and monitoring, and a focus on accuracy, scalability, and observability. Potential areas of work include predicting project risk and its severity; identifying critical tasks in project plans; generating project timelines and scaffolding from blueprints and 3D models; estimating task completion from multimodal information such as camera footage, videos, and voice notes; and recommending risk mitigation or schedule improvements. The engineer will also evaluate existing models and prototype improvements informed by current research. Foresight connects schedules, field progress, and execution data across project stakeholders to forecast delivery and help teams act before delays become missed deadlines. The company serves data-center owners, developers, and general contractors, and reports having paying customers and proven project outcomes.

Requirements

At least 3 years of experience building and shipping machine-learning models in production, with real ownership of the backend.
At least 1 year of experience building and shipping VLM/LLM-based features to production.
Strong Python backend skills, using FastAPI, Django, or similar.
Relational data modeling experience with Postgres, including schema design, indexes, query plans, migrations, and an ORM such as SQLAlchemy.
Experience shipping agentic workflows, RAG systems, and high-volume data pipelines.
Experience with asynchronous work, including queues, background jobs, retries, idempotency, and handling worker failures.
Hands-on AWS experience, including S3, RDS, a managed queue or event service, and containers or serverless.
Ability to write tests and debug production issues using logs, metrics, and traces.
Clear written communication and comfort making pragmatic trade-offs in a fast-moving small team.

Nice To Haves

Experience with workflow orchestration for multi-step jobs, such as Temporal, Step Functions, or Airflow.
Experience with a legacy-to-modern migration or products for regulated enterprise or public-sector customers, including SOC 2 or data residency.
Early-stage or founding-team experience.

Responsibilities

Own features from technical design through release and beyond, measuring adoption and continuously improving them.
Work with full-stack engineers to turn new models into production features customers rely on.
Partner with product, design, and customers to identify the problems most worth solving.
Tune performance across the modeling stack, including accuracy, latency, and reliability.
Raise standards for architecture, testing, observability, and the way the team ships.
Help shape the product and technical roadmap.
Build, train, and deploy models for project-risk prediction, critical-task identification, project planning, progress tracking, and risk mitigation.
Repeatedly evaluate models and prototype methods and models that improve the accuracy of existing features.