Applied AI Engineer

Foresight

. Build, train and deploy machine learning models for risk predictions and AI recommendations .

Posted 11/7/2026full-timeUnited StatesMid-LevelSenior💰 $180,000 - $230,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying Machine Learning models, with a strong focus on risk prediction and optimization. Proficient in backend development using Python and relational data modeling, while effectively collaborating with cross-functional teams to drive product innovation.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython Backend DevelopmentRelational Data ModelingAWS ExperienceVLM/LLM Feature Shipping

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningRisk Prediction ModelsPythonFastAPIDjangoPostgresSQLAlchemyData PipelinesAWS S3AWS RDS

Soft Skills

Clear Written CommunicationPragmatic Decision-Making

Tools & Technologies

FastAPIDjangoPostgresAWSTemporalStep FunctionsAirflow

Industry Keywords

Risk MitigationAgentic WorkflowsRAG SystemsData ResidencySOC 2

Tech Stack

Tools & technologies

AirflowAWSDjangoPostgresPython

About the role

Key responsibilities & impact

Build, train and deploy machine learning models for risk predictions and AI recommendations
Develop risk prediction models for project delays and risk magnitude
Build criticality prediction models to identify tasks most critical to completing projects on time
Create project timelines and scaffolding from blueprints and 3D Replit models
Develop progress-tracking models using camera/video, human/drone, and voice-note data
Build risk-mitigation models that predict optimal paths forward for major projects
Evaluate and prototype models and methods based on current research
Own features from technical design through release, adoption measurement and continuous improvement
Work with Full Stack engineers to turn models into production features
Partner with product, design and customers to identify valuable problems
Tune model-stack performance for accuracy, latency and reliability
Improve architecture, testing, observability and shipping practices
Help shape the product and technical roadmap

Requirements

What you’ll need

3+ years of experience building and shipping Machine Learning models in production, with real ownership of the backend
1+ years of experience building and shipping VLM/LLM based features to production
Strong Python backend skills, including FastAPI, Django or similar
Relational data modelling in 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 async work: queues, background jobs, retries, idempotency, and worker failure handling
Hands-on AWS experience, including S3, RDS, managed queue or event services, 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 small, fast-moving team
Bonus: workflow orchestration experience with Temporal, Step Functions, Airflow or similar
Bonus: legacy-to-modern migration experience or experience with regulated enterprise/public-sector products, including SOC 2 or data residency
Bonus: early-stage or founding-team experience

Benefits

Comp & perks

Health insurance
Dental insurance
Vision insurance
401(k)
PTO
Occasional travel may be required