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Tech Stack
Tools & technologiesAWSAzureCloudDistributed SystemsDockerERPGoGoogle Cloud PlatformKubernetesMySQLNeo4jNoSQLPostgresPythonPyTorchRPARustSQLTensorflow
About the role
Key responsibilities & impact- Design, build, and own the core infrastructure powering our AI agent platform, from data pipelines to production deployment systems.
- Build and scale the backend systems that support high-throughput document processing and data extraction workloads.
- Architect and deploy infrastructure on cloud platforms (AWS, GCP, or Azure) with a focus on scalability, reliability, and cost efficiency.
- Own containerization and orchestration (Docker, Kubernetes) for all production workloads.
- Build and maintain CI/CD pipelines and DevOps practices that let the team ship fast without breaking things.
- Design and manage data pipelines to process and analyze large volumes of documents and unstructured data at scale.
- Build the infrastructure layer connecting AI agents to databases, vector stores, and enterprise systems (ERP, CRM).
- Build and maintain robust, well-documented APIs connecting AI agents with external systems and enterprise software.
- Ensure compliance with data privacy standards (e.g. GDPR, HIPAA) and drive best practices for secure data handling across the infrastructure.
- Build observability and monitoring systems to track infrastructure health, performance, and cost.
- Collaborate with AI/ML engineers, product, and the founding team to make sure infrastructure decisions support fast iteration and production-grade reliability.
- Participate in code reviews, design discussions, and architecture planning to drive infrastructure strategy.
Requirements
What you’ll need- 5+ years of experience in backend or infrastructure engineering
- Proven track record of building and scaling infrastructure in production environments.
- Proficiency in Python.
- Databases: Proficiency in SQL (PostgreSQL, MySQL) and NoSQL (e.g. Document DB, Vector DB).
- Deep experience deploying and scaling large production applications on AWS, GCP, or Azure.
- Containerization and orchestration: Docker, Kubernetes.
- Strong understanding of OAuth2, JWT, and best practices for securing distributed systems.
- Experience with RPA (Robotic Process Automation) tools (bonus skill).
- Familiarity with graph databases (Neo4j) for managing complex workflows (bonus skill).
- Familiarity with Go and Rust (bonus skill).
- Experience working alongside AI/ML teams using frameworks like TensorFlow, PyTorch, or Hugging Face (bonus skill).
Benefits
Comp & perks- Health insurance
- Flexible work arrangements
- Professional development opportunities
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
PythonSQL (PostgreSQL, MySQL)NoSQL (Document DB, Vector DB)OAuth2JWTRPA ToolsGraph Databases (Neo4j)GoRustAI/ML Frameworks (TensorFlow, PyTorch, Hugging Face)