Job Description
About Epiq AI Labs
Epiq AI Labs is the innovation and engineering hub behind Epiq's next-generation AI platform for corporate legal departments and global law firms. We operate as a startup within a global organization, focused on agentic AI systems, knowledge-driven reasoning, and enterprise-scale simulations. We are:
- Fast, experimental, and product-obsessed
- Highly collaborative, low-bureaucracy, deeply technical
- Backed by the resources, data access, and distribution scale of a global ALSP
We are building foundational AI systems intelligent agents, reasoning engines, knowledge bases, and structured workflows that transform how litigation, investigations, compliance, and corporate knowledge work get done.
Our culture is that of a startup: high autonomy, high trust, rapid iteration, and a team that genuinely enjoys working together.
About the Role
You will build and operate the services behind our AI-driven legal platform: asynchronous processing, data pipelines, the data access layer, and authentication and authorization. These are load-bearing systems, owned end to end rather than in slices and as the platform grows, the surface you own grows with it.
Scalability and reliability are requirements, not later additions. Services should absorb growth in tenants, data volume, and concurrency without redesign, tolerate partial failure through retry, idempotency, and graceful degradation, and carry enough instrumentation to diagnose production behaviour. We build on a modern stack and treat design as real work: choices about data models, service boundaries, and processing patterns are reasoned through and documented, made alongside senior engineers rather than inherited from them. The platform is early enough that what you build now will still be in its foundation years from now.
Key Responsibilities
- Implement, test, and operate backend services and APIs that support our agentic AI platform
- Build and maintain message-queue-based asynchronous processing, including retry and idempotency handling, dead-letter processing, and failure recovery
- Contribute to data pipelines that ingest, parse, store, and index large volumes of documents
- Implement authentication and authorization functionality using OAuth2, OIDC, and SAML within established patterns, including role-based access control for multi-tenant environments
- Design and optimize PostgreSQL schemas and queries, and carry out schema migrations against live production traffic
- Integrate search and vector storage technologies such as Solr and Qdrant
- Instrument services with metrics, logs, and traces; monitor service health and participate in the on-call rotation
- Write automated tests, participate in code review, and maintain continuous integration pipelines
- Contribute to technical design documents and participate in design review
- Collaborate with AI engineers, other backend engineers, product management, and security
- stakeholders
Required Qualifications
- 3+ years of professional experience building and operating backend services in production, including responding to production issues
- Strong professional experience in Python development for backend services and APIs.
- Strong professional experience in C#/.NET application development.
- Strong database expertise in SQL with experience in database design, query optimization, stored procedures, and performance tuning
- Experience with at least one major cloud platform (AWS, GCP or Azure)
- Working experience with relational databases, including schema design, query optimization, and migrations (PostgreSQL preferred)
- Experience with asynchronous or event-driven processing, such as message queues (RabbitMQ, Kafka, or comparable) or task workers
- Experience designing and implementing REST APIs consumed by other services or teams
- Experience with automated testing and continuous integration practices
- Familiarity with containerized deployment (C)
- Familiarity with observability practices, including metrics, logging, and tracing
- Demonstrated proficiency using AI tools in software development
- Clear written and verbal communication, and the ability to make progress on ambiguous problems with appropriate guidance
- Stack: Python · PostgreSQL · RabbitMQ · Solr · Qdrant · OAuth2 / OIDC / SAML · Azure · Kubernetes · Docker · Terraform · Prometheus · Grafana · OpenTelemetry.
Preferred Qualifications
- Experience with high-volume data or document processing pipelines
- Experience with infrastructure-as-code tools such as Terraform
- Experience with multi-tenant SaaS architectures
- Exposure to AI/ML systems or data-intensive applications
- Experience in legal technology or another regulated industry
- Experience with authentication and authorization standards (OAuth2, OIDC, SAML)
