Senior ML Engineer

Join a product-minded team building a multitenant SaaS platform with data at its core. You will design and ship the services behind inventory tracking, order generation, optimization, and forecasting.

This role combines data engineering, applied machine learning, backend development, cloud infrastructure, and light frontend work. You will turn high-level product and operational problems into reliable software through research, critical thinking, strong engineering practices, and a consistent focus on data quality.

What You'll Do

• Design and build services supporting inventory tracking, order generation, optimization, and forecasting.
• Develop production services using Python and FastAPI.
• Design and optimize Postgres schemas, queries, migrations, and data access patterns.
• Build internal dashboards, alerts, and lightweight tools using TypeScript and React.
• Package, deploy, monitor, and retrain machine learning models in production.
• Work with Vertex AI for artifact tracking, batch scoring, and online inference.
• Build and operate containerized workloads on Kubernetes in AWS.
• Implement integrations across APIs, flat files, EDI, and database connectors.
• Design reliable data models, reconciliation processes, and data-quality controls.
• Apply multitenant SaaS patterns including tenant isolation, authentication, and RBAC.
• Contribute across platform engineering, data, ML, and UI needs as product requirements evolve.

Required Qualifications

• Strong data engineering background combined with applied ML experience in production.
• Experience developing services with Python and FastAPI.
• Strong SQL and Postgres skills, including schema design, performance tuning, and migrations.
• Experience with TypeScript and React for dashboards, alerts, and lightweight internal tools.
• AWS proficiency, including containerized workloads on Kubernetes, CI/CD, and observability.
• Practical MLOps experience covering model packaging, deployment, monitoring, and retraining.
• Experience with Vertex AI for artifact tracking, batch scoring, and online inference.
• Understanding of multitenant SaaS architecture, including tenant isolation, authentication, and RBAC.
• Experience integrating systems through APIs, flat files, EDI, and database connectors.
• Strong data modeling and reconciliation skills.
• Product mindset, clear communication, critical thinking, and willingness to work across platform, data, ML, and light UI responsibilities.

Preferred Qualifications

• Experience with forecasting and recommendation systems for inventory and ordering.
• Experience designing exception and anomaly alerting.
• Event-driven and queue-based architectures for ingestion and orchestration.
• Experience building data QA tools, lineage capabilities, and audit trails.
• Hands-on exposure to AWS services such as S3, RDS or Aurora Postgres, ECR, EKS, Lambda or Fargate, Step Functions, and EventBridge.
• Kafka or equivalent stream-processing experience.
• Experience building dashboards for partners and corporate users.
• Exposure to chat or chatbot functionality.
• Infrastructure as code experience with Terraform and strong CI/CD practices.

Technical Environment

Python · FastAPI · SQL · Postgres · TypeScript · React · AWS · Kubernetes · Vertex AI · Terraform · Kafka · S3 · RDS/Aurora Postgres · ECR · EKS · Lambda/Fargate · Step Functions · EventBridge · CI/CD · MLOps

Why Join Us

You will work on a data-centric SaaS platform where engineering directly affects core product capabilities, from inventory visibility and ordering to optimization and forecasting. The role offers broad technical ownership across production software, data systems, machine learning, cloud infrastructure, and product-facing tooling.