MLOps Engineer

Tech Sequence is a fast-growing technology startup founded by professionals from the United States and Vietnam, combining international expertise with local market insight to deliver innovative and practical technology solutions. The company specializes in technology consulting, digital transformation, software engineering, business process optimization, and smart infrastructure systems. Its capabilities include AI solutions, ERP and CRM implementation, custom web and mobile application development, ELV solutions, security systems, AI-powered video analytics, access control, network infrastructure, smart parking, and integrated facility technologies. Serving clients across manufacturing, logistics, commercial real estate, retail, and office environments, Tech Sequence helps organizations improve efficiency, strengthen security, optimize business processes, and build scalable technology platforms for sustainable growth. Backed by a leadership team with experience across the United States and Asia-Pacific, Tech Sequence combines startup agility with enterprise-level execution to help businesses transform challenges into measurable business outcomes.

You'll be part of the Digital Engineering / AI team at Tech Sequence, reporting to the Engineering Manager / Head of AI.

This is a Grade G3 role for professionals who own delivery end-to-end with limited supervision. The MLOps Engineer automates and operationalizes the machine-learning lifecycle — building the pipelines, deployment, and monitoring systems that take models from experiment to reliable production. Working with ML, data, and platform teams, this role ensures models are deployed, versioned, monitored, and retrained efficiently and reproducibly across Tech Sequence's products and client solutions. The MLOps Engineer owns the operational side of ML — CI/CD for models, deployment, observability, and governance — and is accountable for reliability, reproducibility, automation, and cost. The ideal candidate combines strong DevOps and software engineering with practical ML knowledge and can build and operate robust, automated ML delivery pipelines.

Ramp up on our systems, codebase, and current projects in your first 30 days Own a defined area of work with minimal supervision by day 60 Deliver a complete piece of work end-to-end by day 90

Design and build automated training, validation, and deployment pipelines (CI/CD for ML). Implement reproducible workflows for data, features, and model versioning. Deploy models to production as scalable, reliable services across cloud and, where needed, edge environments. Manage model registries, rollouts, canary releases, and rollbacks. Build monitoring for model performance, data and model drift, latency, and cost. Set up alerting, logging, and observability, and drive incident response and remediation. Implement model governance, lineage, and compliance across the ML lifecycle. Automate infrastructure provisioning and environment management with infrastructure-as-code. Collaborate with ML, data, and platform teams and participate in code reviews. Contribute to shared MLOps tooling, standards, and best practices.

The MLOps Engineer is evaluated against the skill matrix below — a primary technology stack complemented by secondary and adjacent skills.

Technologies

Python, CI/CD, MLOps tools (MLflow/Kubeflow)

Docker & Kubernetes, cloud (AWS/Azure/GCP), orchestration (Airflow/Argo)

Terraform/IaC, monitoring & drift detection, feature stores, and data engineering

Bachelor's degree in Computer Science, Software Engineering, or a related field; a Master's degree or relevant certification is a strong advantage. 4+ years of DevOps, software, or ML engineering experience, including ownership of operationalizing ML models in production. Strong proficiency in Python, and experience with cloud platforms (AWS, Azure, or GCP) and CI/CD. Strong problem-solving, collaboration, communication, presentation, and English communication skills. Solid understanding of the ML lifecycle, model deployment, and monitoring. Experience with MLOps tools (e.g., MLflow, Kubeflow, SageMaker/Vertex AI) and workflow orchestrators (e.g., Airflow, Argo). Proficiency with containers and orchestration (Docker, Kubernetes), infrastructure-as-code (Terraform), and CI/CD pipelines. Experience with feature stores, model and data drift detection, and large-scale inference. Exposure to data engineering, streaming systems, and cost and GPU optimization.

Automation-minded, reliability-focused, outcome-driven, and comfortable working through ambiguity. Self-driven, adaptable, and passionate about MLOps and continuous learning.

Opportunity to work directly with founders and leadership teams. Exposure to international projects and global clients. Dynamic, collaborative, and fast-growing startup environment. Professional development and career growth opportunities. Competitive salary and performance-based incentives. Flexible and innovation-driven working culture. If you are passionate about artificial intelligence, machine learning, and building intelligent, scalable systems, and contributing to high-impact products and client solutions, and are looking to grow your career in a dynamic international environment, Tech Sequence would love to hear from you.

Send your CV to hr@techsequence.com with the subject line: “Application for MLOps Engineer - [Your Name]”Sincerely, Tech Sequence Recruitment Team