Jobs at Oxford Dynamics

Senior individual contributor responsible for designing, building, deploying, and operating high-availability backend systems. The role focuses on Python and FastAPI services, versioned APIs, event-driven architectures, PostgreSQL performance, security, observability, Docker, and production reliability. The engineer will provide technical leadership, mentor colleagues, translate product goals into technical plans, and contribute to AI integrations and infrastructure as code.

Build and own production-grade agentic AI systems end to end, including multi-agent orchestration, LLM and vision-language model integration, retrieval, memory, tool calling, evaluation, observability, and guardrails. Deploy reliable AI pipelines across cloud, on-premises, secure, air-gapped, and offline environments. Lead technical architecture, write tested Python code, optimize latency and failure behavior, and provide technical direction, documentation, and knowledge transfer.

Own and engineer secure, scalable AWS and Kubernetes platforms across multi-account, multi-region environments. Build infrastructure with Terraform and Helm, automate CI/CD using GitHub Actions and Argo CD, improve reliability through observability and SRE practices, and lead security hardening, compliance audits, disaster recovery, cost optimization, and incident response. Mentor developers and promote DevSecOps practices while supporting high-availability platforms for mission-critical AI systems.

Own the biotech client account end to end, including relationship management, product priorities, delivery, team coordination, commercial performance, adoption, and account growth. Translate client and market needs into roadmaps, manage scope, risks, budgets, vendors, and milestones, and partner with technical leads on delivery decisions. Lead Agile ceremonies, reporting, stakeholder communication, and scalable processes for long-term client partnerships.

Research Engineer applying frontier machine learning and reinforcement learning research to production AI capabilities. Responsibilities include model design, training, evaluation, RLHF, multi-objective optimization, LLM post-training, reward modeling, auditing, and translating prototypes into reliable systems. The role also involves customer engagement, technical communication, and shaping research and product strategy.

Build and own production-grade agentic AI systems end to end, including multi-agent orchestration, LLM and vision-language model integration, retrieval, memory, guardrails, evaluation, observability, and deployment pipelines. The role requires designing reliable systems for cloud, on-premises, air-gapped, and offline environments; handling large multimodal datasets; and setting technical direction for other engineers. Strong Python, software engineering, production AI/ML, and secure deployment experience are required.