Forward Deployed Engineer | Python | API's | AWS | Docker | Kubernetes | Hybrid | London, UK

Enigma; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Benefits offered by this job

Private health insurance

Pension contributions

Hybrid work arrangement

Travel opportunities

Job summary

Enigma seeks a Forward Deployed AI Engineer to bridge generative AI models with customer environments in London. You will deploy, integrate, and optimise AI technology within pharmaceutical and biotech workflows, owning deployments from discovery to production.

You will work across cloud infrastructure, API integrations, and model serving, collaborating with customers and internal teams to translate complex concepts and deliver scalable, secure solutions.

Qualifications

• Strong academic background in Computer Science, Machine Learning, AI or similar quantitative field (BSc, MSc or PhD).
• Experience building systems that interact with large AI models through APIs.
• Hands-on experience deploying AI/ML infrastructure and model serving at scale.
• Ability to translate complex technical concepts for technical and non‑technical stakeholders.
• Proficiency with cloud platforms, especially AWS; familiarity with Azure or Google Cloud is a plus.

Responsibilities

• Lead end-to-end deployment of AI models into customer environments.
• Design production-ready API integrations, data pipelines, and model-serving infra.
• Collaborate with customer engineering and scientific teams to gather requirements and troubleshoot.
• Ensure deployments meet security, scalability, reliability, and performance standards.
• Create technical documentation, implementation guides, and best-practice resources.

Skills

API integration

Cloud infrastructure

Communication

Collaboration

Education

PhD in Computer Science or related

Tools

Python

Docker

Kubernetes

AWS

CI/CD

Job description

Forward Deployed Engineer | Python | API's | AWS | Docker | Kubernetes | Hybrid | London, UK

The Opportunity

We are looking for a Forward Deployed AI Engineer to serve as the critical bridge between cutting-edge generative AI models and the customers who rely on them. You will work directly with pharmaceutical and biotechnology organisations to deploy, integrate, and optimise AI technology within their scientific workflows.

This is a highly technical, customer-facing role that combines deep infrastructure expertise with a passion for solving real-world challenges in drug discovery and protein engineering.

You will partner closely with customers to understand their technical environments and ensure seamless integration of our generative biology platform into their existing systems. You will own the full lifecycle of customer deployments, from initial technical discovery through production implementation and act as the voice of the customer by providing feedback to internal product, engineering, and research teams.

About the Organisation

We are developing next-generation AI models that advance the understanding and application of biology. Our multidisciplinary team brings together expertise in machine learning, computational biology, software engineering, and scientific research to tackle complex challenges at the intersection of AI and life sciences.

We value scientific excellence, collaboration, continuous learning, and interdisciplinary innovation. Our teams work across multiple international locations, with regular opportunities to collaborate in person and remotely.

We are looking for curious, mission-driven individuals who are excited by ambitious technical challenges and motivated to create meaningful real-world impact.

About You

You will ideally have the following:

• A strong academic background in Computer Science, Machine Learning, Artificial Intelligence, or another quantitative discipline (BSc, MSc, or PhD)
• Experience building systems that interact with large AI models through APIs
• Hands-on experience designing, deploying, and maintaining infrastructure for large-scale model serving
• Experience deploying AI solutions for external customers and translating complex technical concepts for both technical and non-technical stakeholders
• Strong knowledge of cloud infrastructure, particularly AWS, with exposure to platforms such as Azure or Google Cloud
• Experience with Docker, Kubernetes, CI/CD pipelines, and cloud-native architectures.
• Excellent communication and collaboration skills, with the ability to work effectively across technical and business teams
• A proactive, adaptable mindset and the ability to manage multiple customer engagements in a fast-paced environment

The following would be advantageous:

• Experience applying machine learning within computational biology, protein design, or related life sciences
• Contributions to generative AI research, open-source software, publications, or production AI systems
• Experience building secure, reliable enterprise software that meets production requirements.
• Familiarity with pharmaceutical or biotechnology environments, including scientific workflows, data governance, or regulatory considerations

Key Responsibilities

Customer Deployment & Integration

• Lead end-to-end deployment of AI models into customer environments
• Design and implement production-ready API integrations, data pipelines, and model-serving infrastructure
• Work closely with customer engineering and scientific teams to gather requirements, troubleshoot issues, and deliver technical solutions
• Ensure deployments meet enterprise standards for security, scalability, reliability, and performance

Customer Success & Product Feedback

• Serve as the primary technical contact for assigned customers
• Build trusted relationships with scientific and engineering stakeholders.
• Gather customer feedback and translate it into actionable recommendations for internal product and engineering teams
• Contribute to product roadmap discussions by sharing insights from real-world deployments.
• Develop technical documentation, implementation guides, and best-practice resources

Professional Development

• Stay current with advances in machine learning infrastructure, cloud technologies, and model-serving practices
• Build domain knowledge in computational biology and related scientific areas relevant to the platform
• Contribute to internal knowledge sharing through technical presentations, documentation, and learning sessions

We offer a competitive compensation and benefits package, which may include:

• Private health insurance
• Retirement or pension contributions
• Generous annual leave and family-friendly policies
• Hybrid working arrangements
• Opportunities for travel and international collaboration
• A collaborative environment with opportunities to work on cutting-edge AI applications in life sciences

We are committed to fostering an inclusive workplace and welcome applications from candidates of all backgrounds. We believe that diverse perspectives, experiences, and ideas are essential to building exceptional teams and delivering meaningful innovation.

Forward Deployed Engineer | Python | API's | AWS | Docker | Kubernetes | Hybrid | London, UK