
AI Platform Integration Engineer
- Boston, MA
- Permanent
- Full-time
- Platform Evaluation & Adoption:
- Evaluate agentic AI software engineering platforms for coding, testing, CI/CD, and operational support use cases.
- Lead proofs-of-concept (PoCs) and pilot programs to validate integration and usability within our cloud and managed services ecosystems.
- Collaborate with internal teams to establish best practices for secure and effective use of AI-driven development tools.
- Engineering & Integration:
- Develop automation, extensions, or integration layers between AI coding agents and internal platforms.
- Build prototypes and contribute to internal tooling that accelerates the packaging, deployment, and monitoring of our products as managed services or SaaS.
- Collaboration & Enablement:
- Serve as a technical liaison between Managed Services and Product Engineering teams.
- Provide guidance and training to teams adopting new AI coding workflows.
- Contribute to governance and compliance efforts around AI usage in software engineering.
- Strategic Contribution:
- Influence and help define the architecture and roadmap for delivering InterSystems' products as scalable, resilient managed services.
- Drive continuous improvement in developer experience and software lifecycle velocity through AI augmentation.
- 5+ years of experience in software engineering or platform engineering roles.
- Hands-on experience with cloud-native platforms (AWS, Azure, or GCP), including a deep understanding of services relevant to AI/ML workloads (e.g., Amazon Q, Microsoft Copilot, Azure Machine Learning, Google Cloud AI Platform, etc.).
- Proficiency with containerization technologies (Docker) and orchestration with Kubernetes (including experience with managed Kubernetes services like EKS, AKS, or GKE).
- Strong experience designing, implementing, and maintaining CI/CD pipelines (e.g., Jenkins, GitLab CI, GitHub Actions, Azure DevOps).
- Exposure to or direct experience with AI-powered development tools or LLM-based software agents (e.g., GitHub Copilot, Tabnine, Amazon CodeWhisperer, Windsurf/Codeium, etc.).
- Strong programming skills (Python, Go, or Java preferred).
- Demonstrated ability to lead technical evaluations, prototypes, and cross-functional initiatives.
- Excellent communication skills and the ability to operate effectively across engineering, operations, and leadership teams.
- Experience in building or operating SaaS platforms or managed services in a regulated industry.
- Familiarity with security, compliance, and governance concerns related to AI in enterprise environments, including data privacy and model explainability
- Background in DevOps, SRE, or Platform Engineering is a strong plus.
- Proficiency with Infrastructure as Code (IaC) tools such as Terraform or CloudFormation
- Experience with API design and development (RESTful APIs, GraphQL, gRPC) for integrating AI services
- Knowledge of monitoring and observability tools (e.g., Prometheus, Grafana, ELK stack, Datadog) for cloud-native AI applications
- Understanding of cloud security best practices, including IAM, network security, and data encryption at rest and in transit