Senior Software Engineer, Agentic Platform, AI Labs

At Epiq, your work contributes to complex, global legal outcomes. You’ll join a values‑driven community where integrity guides decisions, relentless service sets the bar, and we thrive on big challenges together. We invest in your growth with enterprise‑wide learning and mobility. We celebrate who you are, and we respect life beyond work with flexibility that’s recognized externally. Enabled by modern platforms and AI, you’ll do the most meaningful work of your career and see your impact at scale.

Job Description:

Epiq AI Labs is the innovation and engineering hub behind Epiq’s next-generation AI platform for corporate legal departments and global law firms. Operating with the speed and autonomy of a startup and the resources of a global alternative legal services provider, the team builds intelligent agents, reasoning engines, knowledge systems, and structured workflows for litigation, investigations, compliance, and corporate knowledge work.

The team is highly collaborative, deeply technical, and focused on rapid iteration, thoughtful design, and end-to-end ownership.

The Opportunity

You will build the agentic systems at the center of Epiq AI Labs’ legal AI platform, including the orchestration runtime used by other teams, the retrieval layer agents reason over, and the evaluation infrastructure used to measure quality. You will own systems from initial design through production operation.

Legal workflows require a high standard of correctness, traceability, confidentiality, and tenant isolation. You will establish the primitives for orchestration, grounded retrieval, and measurable quality that other engineering teams will build on. This role will be in the office 3~4 days a week.

Essential Job Responsibilities

• Design the agentic platform used by other teams, including orchestration, tool registration and permissioning, durable state and memory, sandboxing, and trace-level observability.

• Build agent workflows with multi-step reasoning, tool use, human-in-the-loop checkpoints, and recovery from partial failure during long-running execution.

• Evaluate developments in model capabilities, agent-design patterns, and evaluation practices, and translate them into platform architecture.

• Design retrieval-augmented generation systems with hybrid search over legal corpora and citation grounding to specific sources.

• Build evaluation infrastructure, including golden datasets, regression harnesses, and offline and online evaluation loops.

• Establish model-operations practices for routing, latency and cost budgets, prompt and context versioning, caching, and production reliability.

• Build scalable backend services and APIs; produce technical design documentation; partner with product, research, security, and legal domain experts; and strengthen engineering standards.

Required Qualifications

• 5+ years of software-engineering experience with demonstrated depth in backend, distributed, or AI systems.

• Demonstrated experience delivering and operating LLM-based applications in production at scale, beyond prototypes or proofs of concept.

• Demonstrated experience with agentic architectures and orchestration frameworks.

• Demonstrated experience with retrieval-augmented generation systems at scale.

• Strong production-level proficiency in Python.

• Demonstrated experience evaluating agentic-system accuracy and performance.

• Experience designing services and APIs consumed by other engineering teams.

• Experience with observability tooling such as Prometheus, Grafana, or OpenTelemetry.

• Experience operating in containerized cloud environments.

• Strong system-design and architecture experience, including production of technical design documents.

• Excellent written and verbal communication, including the ability to work effectively with non-technical domain experts and stakeholders.

• Demonstrated proficiency using AI tools in software development.

Preferred Qualifications

• Experience with fine-tuning, model adaptation, or systematic evaluation across model families.

• Experience with large-scale data pipelines or document-processing systems.

• Experience with multi-tenant SaaS architectures and data-isolation requirements.

• Experience in a startup or scale-up engineering environment.

• Experience in legal technology, enterprise workflow, or knowledge-management systems.