Title
MLOps Engineer
Quick Summary
TidalForge Systems is hiring an MLOps Engineer to help transform research notebooks into reliable, production-grade machine learning services. The role focuses on automating training, packaging, deployment, and monitoring so models can be delivered quickly and safely. The ideal hire is pragmatic, learns fast, and enjoys building clean pipelines that other engineers can trust.
Project Category or Industry
Artificial Intelligence and Data Platforms for SaaS and analytics products.
Type
Full-time employment.
Experience Level
Entry-level to mid-level. Strong graduates and early-career engineers are welcome; prior internships or personal projects demonstrating practical MLOps skills will be valued.
Duration
Permanent role.
Location
Remote-first with optional hybrid collaboration days in London and Singapore. Successful candidates maintain at least four hours of overlap with UTC to UTC+8.
Salary
USD 72,000–98,000 base depending on location and experience, plus benefits and an annual performance bonus.
Payment Mode
Monthly payroll. For select jurisdictions where payroll is unavailable, a compliant contractor arrangement can be used.
Hiring Company Name
TidalForge Systems
Required Skills or Tools
Candidates should be comfortable working with Python and modern ML frameworks, understand containerization and orchestration, and know how to design observability for data and models. Familiarity with cloud services and CI/CD is important; experience with experiment tracking and feature stores is a plus.
Role Overview and Objectives
This role exists to make model delivery boring—in the best way. You will take models developed by data scientists and machine learning engineers and turn them into reproducible, testable, and observable services. Success means faster release cycles, predictable rollouts, and measurable improvements to model quality in production.
Core Responsibilities and Expected Deliverables
You will build training and inference pipelines, define and maintain CI/CD for model artifacts, and standardize packaging practices for batch and real-time serving. You will introduce automated model evaluation gates, data validation checks, and model versioning that integrates with our release process. Expected outputs include production-ready containers, IaC-backed deployment manifests, monitoring dashboards and alerts, runbooks, and concise documentation for handoffs.
Required Experience and Preferred Qualifications
A solid foundation in Python and software engineering practices is required, including version control, unit testing, and code review. Experience with one deep learning or classical ML framework such as PyTorch, TensorFlow, or scikit-learn is useful. Exposure to Kubernetes, Docker, and a major cloud provider (AWS, GCP, or Azure) will help you be productive quickly. Preferred qualifications include familiarity with MLflow or Weights & Biases, Airflow or Prefect for orchestration, FastAPI for serving, and tools like DVC or Feast. Certifications are not mandatory, but cloud fundamentals or Kubernetes coursework will stand out.
Tools or Platforms to Be Used
Day-to-day work will center on Python, Docker, Kubernetes, GitHub Actions, MLflow or Weights & Biases, and a mix of AWS and GCP services. You will use Terraform to define infrastructure, Prometheus and Grafana for metrics, and OpenTelemetry-compatible tooling for traces and logs. For data pipelines, the team commonly uses Airflow and dbt; for online serving, you may work with FastAPI or gRPC endpoints.
Language Requirement
Professional English is required. Additional languages are helpful when collaborating with partners in EMEA and APAC.
Communication Style
Collaboration is written-first. The team uses GitHub for reviews and issues, Slack for daily coordination, and Zoom for design and incident reviews. Clear, succinct documentation is expected for all changes.
Time Commitment or Working Window
This is a 40-hour-per-week role with flexible hours. Maintain a predictable daily window that overlaps at least four hours with the core team between 09:00 and 17:00 in your local time.
Payment Terms
Salary is paid monthly via payroll. If engaged as a contractor in a supported region, invoices are paid on net-30 terms after acceptance of deliverables and timesheets.
Evaluation Criteria
Applicants are shortlisted based on a portfolio or code samples that demonstrate pipeline thinking and reliability. The process includes a brief screening call, a practical exercise focused on packaging and deploying a small model service, a technical discussion on observability and rollout strategies, and a final interview covering collaboration and impact. References may be requested.
Other Requirements
New hires sign a confidentiality agreement and follow secure-by-default engineering practices. Reasonable background checks may be conducted where permitted. Light time-tracking is used for distributed coordination and post-incident analyses. Occasional on-call participation for model platform changes is shared across the team.
About TidalForge Systems
TidalForge Systems is a product engineering company that builds AI-enabled data platforms for digital businesses. Our teams combine rigorous software craftsmanship with practical machine learning to help customers ship features that matter. We operate primarily in the technology and analytics sector with clients across North America, Europe, and Asia.
Headquartered in London with a distributed workforce across EMEA and APAC, TidalForge Systems is privately held and founder-led. Learn more at https://www.tidalforge.io and reach the hiring team at hiring@tidalforge.io.