Machine Learning Engineer

Ampcus, Inc; Pleasanton (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

A technology consulting firm is seeking a Machine Learning Engineer to design and maintain MLOps pipelines, develop FastAPI microservices, and operationalize ML models. The ideal candidate should have strong Python skills, practical MLOps experience, and knowledge of containerization and orchestration tools. You will work cross-functionally to integrate ML services into client applications while ensuring best practices for model delivery. Join us to contribute to innovative solutions in an agile environment.

Qualifications

• Strong Python engineering skills and production experience with FastAPI.
• Proven MLOps experience packaging and serving models as APIs.
• Hands-on CI/CD for ML including automated testing.

Responsibilities

• Design and maintain MLOps pipelines for data prep and deployment.
• Develop FastAPI microservices for model inference.
• Monitor model and data drift, automating workflows.

Skills

Python engineering

MLOps experience

CI/CD for ML

Containerization

RESTful API design

Collaboration

Tools

FastAPI

GitHub Enterprise

Docker

Kubernetes

Argo CD

MLflow

Job description

Ampcus Inc. is a certified global provider of a broad range of Technology and Business consulting services. We are in search of a highly motivated candidate to join our talented Team.

Job Title: Machine Learning Engineer
Location(s): Pleasanton, CA.

Key Responsibilities:

• Design, build, and maintain end-to-end MLOps pipelines for data prep, training, validation, packaging, and deployment.
• Develop FastAPI microservices for model inference with clear API contracts, versioning, and documentation.
• Define and implement deployment strategies on AKS (blue/green, canary, shadow; champion/challenger) using GitOps with Argo CD.
• Architect and evolve a self‑serve MLOps platform (standards, templates, CLI/scaffolds) enabling repeatable, secure model delivery.
• Operationalize scikit‑learn and other frameworks (e.g., PyTorch, XGBoost) for low‑latency, scalable serving.
• Implement CI/CD for ML (test, security scan, build, package, promote) using GitHub Enterprise and related tooling.
• Integrate telemetry and observability (logging, metrics, tracing) and establish SLOs for model services.
• Monitor model and data drift; automate retraining, evaluation, and safe rollout/rollback workflows.
• Collaborate with software engineers to integrate ML services into client applications and shared platforms.
• Champion best practices for code quality, reproducibility, and governance (model registry, artifacts, approvals).

Required Qualifications:

• Strong Python engineering skills and production experience building services with FastAPI.
• Proven MLOps experience: packaging, serving, scaling, and maintaining models as APIs.
• Hands‑on CI/CD for ML (GitHub Enterprise or similar), including automated testing and release pipelines.
• Containerization and orchestration expertise (Docker, Kubernetes) with production deployments on AKS.
• GitOps experience with Argo CD; practical knowledge of deployment strategies (blue/green, canary, rollback).
• Solid understanding of RESTful API design, microservices patterns, and API contract governance.
• Experience designing or contributing to an MLOps platform (standards, templates, tooling) for repeatable delivery.
• Ability to work cross‑functionally with data scientists, software, and platform/SRE teams.

Preferred Qualifications:

• Minimum 2+ years related experience.
• Experience with ML lifecycle tools (MLflow or similar for tracking/registry) and feature stores.
• Exposure to Databricks and enterprise data/compute environments.
• Cloud experience on Azure (preferred), plus GCP familiarity and managed ML services.
• Familiarity with Agile practices; experience with Helm/Kustomize, secrets management, and security scanning.

Ampcus is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veterans or individuals with disabilities.