Senior Full-Stack AI Engineer (contract) at Oxford Dynamics

Vale of White Horse, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

16 Mar, 26

Salary

0.0

Posted On

16 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Development, Agentic AI, Multi-Agent Systems, LangGraph, AWS, Terraform, Kubernetes, CI/CD, Python, Bash, Go, NLP, MLOps, Observability, Security, Automation

Industry

Defense and Space Manufacturing

Description

This role is outside IR35 Oxford Dynamics – Who We Are Founded in 2020, Oxford Dynamics has grown rapidly to be recognised globally for its innovative AI & Robotics technologies designed for mission critical environments. Helping drive the future of decision making at speed from a human or autonomous platform perspective, Oxford Dynamics is a brand that is recognised and respected by Defence & Security organisations by delivering solutions that solve customer problems. Our founders instil the business with passion and purpose meaning we have a “can do” culture and roll up our sleeves up to get the job done. So, what is it that we do? Our flagship AVIS AI software framework fuses multi-modal data - text, imagery, telemetry etc to allow human-like conversations with complex data. Our unique STRIDER robotic platform performs autonomous tasks in hazardous environments, enabling rapid, informed decisions while protecting people and assets. Our vision is to converge the worlds of robotics and AI to enable machines to sense, understand and act in complex environments. We work with an expanding range of clients to help protect a range of nations’ citizens. Role Overview – What you will be doing here We are seeking a senior full-stack AI/ML engineer with deep expertise in agentic AI frameworks such as LangGraph (or equivalent) and proven commercial experience building complex, multi-agent systems with more than at least four agents. This role blends cutting-edge AI research with hands-on deployment leadership. You will design and optimise agent-based AI pipelines, deploy them into cloud and edge environments, and own the infrastructure that keeps them secure, scalable, and cost-efficient. You will be as comfortable in a Jupyter Notebook as you are in a Terraform config, with the ability to turn research ideas into production systems that run reliably in constrained environments. Key Responsibilities To include, but not limited to: AI & Agentic Development Architect, build, and optimise multi-agent AI systems using LangGraph or similar frameworks. Integrate LLMs into agent workflows for search, reasoning, and automation at scale. Conduct applied research in LLMs, NLP, and agent orchestration, with a focus on edge and offline deployments. Build production-ready ML pipelines from ingestion to inference. Infrastructure & Deployment Implement, and maintain multi-account, multi-region AWS environments using IaC (Terraform, Helm, CloudFormation). Manage Kubernetes clusters (EKS/OpenShift), service meshes, and container registries. Implement GitHub Actions / Argo CD pipelines for automated, zero-touch deployments. Lead security hardening efforts using GuardDuty, CloudWatch, IAM best practices. Set up observability stacks for proactive monitoring and performance tuning. Own backup, disaster recovery for services that youʼve created. Cross-Functional & Process Collaborate closely with other engineers, product managers and CTO. Mentor engineers on agentic AI design best practices. Present technical findings and research outcomes to technical and non-technical stakeholders. What you can bring to Oxford Dynamics Youʼll be working at the intersection of advanced AI research and real-world infrastructure, shaping how Oxford Dynamics brings agentic AI systems to production in challenging environments. If you thrive on building multi-agent systems, deploying them securely at scale, and experimenting with new ways to run AI at the edge, youʼll find both the technical depth and creative freedom youʼre looking for here. Required Skills & Experience MOD Security Clearance (or ability to obtain). Commercial experience delivering LangGraph or equivalent agentic AI projects with more than four agents in production. Strong grasp of multi-agent orchestration, memory, and tool integration. Proficiency in Python for AI development and Bash/Go for automation. Familiarity with AWS core services (EC2, VPC, IAM, S3, ALB/ELB, CloudFront, ECR/ECS, Elastic Beanstalk, Control Tower). Familiarity with IaC (Terraform, Terraform Cloud, CloudFormation) and expertise in containerisation (Docker, Docker Compose). Solid CI/CD engineering with GitHub Actions and/or Argo CD. Preferred / Bonus Research experience in edge AI or constrained/offline deployments. MLOps experience (Sagemaker, Kubeflow, ZenML). Experience building RESTful services around AI pipelines. ISO 27001, NIST SSDF, OWASP SAMM, or GDPR compliance literacy. Experience with AWS Karpenter, Prometheus, or similar observability stacks. Soft Skills Research-driven mindset, eager to experiment and iterate. Able to bridge the gap between cutting-edge AI research and practical deployment. Strong communicator with the ability to mentor and influence across teams. Comfortable owning projects from concept through production deployment. Why Oxford Dynamics? Join the most exciting growth area in the UK: AI and Robotics! Every member of the Oxford Dynamics team has a major impact on the products and services we provide. Regardless of job title, you’ll get to make a real difference and learn from colleagues about all areas of our business. Benefits include: Rapid career progression and personal growth Opportunity to shape the future of a fast-growing, successful, early-stage business Flexible working hours Hybrid working model Oxford Dynamics is committed to creating an inclusive team experience for all. Regardless of race, gender, religion, sexual orientation, age, disability, or parental status, we believe our work is at its best when everyone feels free to be their authentic self.

Responsibilities

The role involves architecting, building, and optimizing multi-agent AI systems, as well as deploying them into cloud and edge environments. The engineer will also lead security hardening efforts and set up observability stacks for monitoring and performance tuning.