11 days ago
Base Salary
$150k - $230k/yr
Responsibilities
- Lead the architecture and implementation of a knowledge graph and multimodal Graph API for human, service, and agentic workflows.
- Own storage, indexing, query, ingestion, and compute infrastructure across the data lifecycle.
- Define durable patterns for schema modeling, relationships, lineage, identity, and schema evolution.
- Build APIs, SDKs, tools, examples, diagnostics, and integrations for data collection, aggregation, and agentic access.
- Establish reference architectures, deployment patterns, benchmarks, operational guidance, and portable platform capabilities.
- Partner with autonomy, ML, test, infrastructure, product, and customer-facing teams to turn workflows into reusable platform capabilities.
- Evaluate emerging technologies and make build-versus-buy decisions while guiding implementation across teams.
- Raise standards for observability, performance, reliability, security, data integrity, disaster recovery, and lifecycle management.
Requirements
- Significant experience designing and operating distributed data solutions, storage systems, or data-intensive backend services.
- Strong software engineering skills and production experience with languages such as Go and Python.
- Deep understanding of data modeling, API design, schema evolution, identity, consistency, indexing, query planning, and data lifecycle concerns.
- Experience with multiple storage modalities, including relational or graph databases, object storage, analytical or columnar systems, and file storage.
- Experience designing reliable ingestion and access paths for high-volume or operationally important data.
- Strong understanding of Kubernetes, Linux, networking, security, storage, observability, and distributed-systems fundamentals.
- Experience deploying data infrastructure across cloud or customer-managed environments using Infrastructure as Code and platform engineering practices.
- Ability to evaluate technologies through prototypes, benchmarks, operational requirements, and lifecycle cost.
- Experience defining architecture and technical standards while remaining hands-on in implementation and debugging.
- Demonstrated collaboration with ML researchers, autonomy engineers, test teams, platform engineers, and product stakeholders.
- Preferred experience includes graph-backed retrieval, structured RAG, provenance-aware context, S3-compatible APIs, Apache Arrow, Parquet, OpenAPI, AsyncAPI, WebSockets, Terraform, Helm, GitOps, Ray, Kafka, NATS, Redpanda, ML data lifecycle systems, observability, distributed tracing, data security, governance, and auditability.
Benefits
- Full-time regular employee offer package includes benefits, bonus, equity, and pay within the listed range.
- Employment is contingent on a cleared background and possible reference check.
- The role is part of a full-time regular employee opportunity; temporary employee benefits are available after 60 days where applicable.
Tech Stack
Categories
BackendData Engineering
About Shield AI
Founded in 2015, Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide.