Machine Learning Engineer

• Design build deploy and maintain production machine learning systems including reinforcement learning components and intelligent optimization-driven features.

• Architect scalable ML pipelines for training validation deployment monitoring and automated retraining.

• Maintain and expand operations for simulation (Monte Carlo Bayesian Networks) and optimization engines (linear constraint CP-SAT) for continued reliable service.

• Own ML Ops and AI Ops practices including CI/CD for models automated testing model validation performance monitoring drift detection observability and governance frameworks.

• Refactor and harden existing AI systems to improve scalability latency cost efficiency and fault tolerance.

• Build and maintain data pipelines and feature engineering workflows that support reliable and reproducible model training.

• Collaborate closely with product and engineering teams to translate resilience use cases into scalable maintainable ML-powered product capabilities.

• Contribute to the design of Fusion's ML architecture infrastructure standards and long-term intelligent systems roadmap.

• Strong software engineering foundation with hands-on experience building and deploying machine learning systems in production environments.

• Experience designing ML architectures APIs and services that integrate with enterprise SaaS platforms.

• Deep understanding of model lifecycle management: experimentation validation deployment monitoring retraining and versioning.

• Experience with reinforcement learning decision systems simulation modeling or optimization techniques.

• Strong experience building scalable data and feature pipelines using cloud-native tools (e.g. Azure Snowflake dbt Salesforce integrations or similar platforms).

• Proficiency in writing clean maintainable well-tested code with version control CI/CD and observability best practices.

• Familiarity with containerization and distributed systems (Docker Kubernetes serverless architectures or similar).

• Ability to design modular extensible ML systems that evolve alongside product requirements.

• Strong communication skills and the ability to explain system behavior tradeoffs and architectural decisions to technical and non-technical stakeholders.

• Complete onboarding and gain familiarity with Fusion's resilience domain existing product line simulation and optimization engines.

• Contribute code to existing ML systems and participate in production improvements.

• Review and assess current ML pipeline and deployment practices.

• Design and deploy at least one production-ready ML component or reinforcement learning module.

• Improve reliability performance or scalability of existing intelligent systems.

• Implement monitoring validation and automated testing for one production AI/ML system.

• Own and deliver a production-grade intelligent capability (e.g. adaptive optimization engine reinforcement-driven decision module or production-trained GPT workflow).

• Establish baseline ML Ops standards for model deployment monitoring retraining and governance.

• Lead architectural improvements to Fusion's ML infrastructure.

• Propose and prototype new ML-driven product capabilities that extend Fusion's resilience intelligence platform.

What We Do

Fusion Risk Management is recognized as the most innovative and fastest growing provider of cloud-based enterprise software for business continuity risk management IT disaster recovery and crisis management. Fusion is transforming the industry and has been named a leader in Gartner's Magic Quadrant for Business Continuity Management software.

Why Work With Us

Fusion provides a highly collaborative work environment where motivated employees can advance their careers and contribute to Fusion’s success. Work-life balance is of high importance at Fusion. We are committed to fostering an environment of trust inclusion transparency innovation and one that encourages hard work passion and having fun.

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