AI/ML Ops Engineer

Cloud-based ML Infrastructure (AWS)

Model Development, Evaluation, & Deployment Operations (SageMaker, Bedrock)

Inference Streams & Event-Driven Processing (Kafka, Spark)

MLOps Workflows (MLFlow, Sagemaker Pipelines)

Infrastructure as Code & Pipeline Automation (Terraform, AI CI/CD, GitHub Actions)

ML Governance (Data, Model, & Feature Versioning, Monitoring & Testing)

Containerized Services (Docker, Kubernetes, ECS/EKS)

Scripting Languages (Python, Bash)

Query Languages (SQL, SparkSQL)

GitFlow, CI/CD workflows & DevOps best practices

Experience with AI-Assisted development life cycle

Building high-availability, production-grade systems with strong visibility and alerting baked in from day one

Transformer Neural Networks

Agile Scrum/Kanban

Anthropic, OpenAI, LiteLLM APIs and SDKs

Experience in Cybersecurity, IoT, or NLP fields

Grafana or CloudWatch (observability tooling)

Own the AI/ML loop end-to-end at scale — across all pipelines, from model training through deployment, monitoring, and retirement.

Develop, optimize, and deploy ML models, drawing on direct, hands-on experience training models yourself.

Design, build, and administer model-building and serving infrastructure, taking trained scripts and standing them up as live endpoints that can accept real-time requests.

Implement ML workflows as containerized Infrastructure as Code, using Terraform, GitHub Actions, Docker, and Kubernetes.

Build and automate standardized container pipelines for training, feature engineering, and inference channels, with CI/CD managed through GitHub.

Own test strategy across the full ML pipeline — model validation, integration, load, and deployment testing — as the team's testing scope continues to expand.

Build visibility and alerting into every deployed pipeline and hold all AI/ML solutions to a production-grade, highly-available, well-monitored bar.

Develop ML governance utilities for oversight and administration of deployed infrastructure.

Implement data, feature, and model lifecycle best practices.

Contribute to AI architecture and design decisions, taking primary ownership of ML pipeline work.

Collaborate closely with cross-functional teams, including Engineering, Blackpoint Cyber's Security Operations Center (SOC), and the Adversary Pursuit Group (APG).

Blackpoint Cyber welcomes and encourages applications from qualified individuals of all races, colors, religions, sex, sexual orientation, gender identity or expression, national origin, age, marital status, or any other legally protected status. We are committed to equality of opportunity in all aspects of employment.

For eligible employees in the US, Blackpoint offers competitive Health, Vision, Dental, and Life Insurance plans, a robust 401k plan, Discretionary Time Off, and other minor perks. International employees receive competitive benefits in accordance with local market standards and applicable country requirements.

Blackpoint believes all employees should share in the company’s success – equity participation is available to employees globally, with program details varying by location and employment structure.