Sr. Software Engineer (AI/ML)

We're building a new engineering team under the SVP, Engineering & AI/ML to accelerate Blackpoint Cyber's ML/AI capability. As a Senior Software Engineer on this team, you'll own back-end systems and data/ML pipelines end-to-end, work cross-functionally across engineering, and be mentored directly by the SVP in applied ML and Generative AI.

• Lead and drive back-end projects across the stack, including Python and Node.js/Go services hosted on Kubernetes, owning delivery end-to-end.
• Build Generative AI workflows and deployments
• Build, optimize, and maintain data pipelines powering AI/ML workflows and model deployments, working independently and devising solutions based on established engineering patterns.
• Design and consume APIs (REST, GraphQL, or gRPC), balancing performance, scale, and resiliency.
• Contribute to architecture and design decisions for AI/ML-enabled features, partnering with the SVP, Engineering & AI/ML and other engineering leads.
• Mentor other engineers and lead them through complex, cross-functional projects.
• Communicate technical solutions and progress directly to leadership, aligning them to business objectives.

• 5-7 years of back-end software engineering experience, with a track record delivering scalable, high-performance systems in fast-paced, enterprise environments.
• Python required. Familiarity with TypeScript and/or Java.
• Strong experience with microservices architecture on Kubernetes.
• Strong database design/performance tuning across SQL (PostgreSQL, MySQL) and NoSQL (MongoDB).
• Master's degree in Computer Science, Machine Learning, or a related field, or demonstrated equivalent applied ML experience.
• Familiarity with ML/AI concepts and workflows (MLOps, model deployment/monitoring) — we're not requiring deep ML specialization; strong engineers with the right foundation and appetite to grow into this space are encouraged to apply.
• Deep understanding of back-end security best practices: securing APIs, handling sensitive data, authentication/authorization (OAuth, JWT).
• Comfortable in an Agile environment; able to break down large initiatives independently.

• Cloud infrastructure & operations (AWS); data streaming/event-driven processing (Kafka, Spark).
• Experience with LLM/agentic AI development pipelines.
• Cybersecurity, IoT, or NLP domain experience (not required).