AI Backend Engineer
We offer the industry’s only platform that fuses customer identity and anti-fraud solutions – customer identity management, identity verification, and fraud prevention.
We sell to industries with large, consumer-facing businesses such as: banking, financial services, insurance, fintech, gaming, ecommerce/retail, telco / media, utilities, etc.
About the Role:
As the AI Backend engineer at Transmit Security, you will join a team of highly skilled machine learning engineers in developing and deploying advanced AI/ML solutions that power our identity and security products. You’ll utilize technical skills to drive innovation, ensure delivery of high-impact projects, and scale our data-driven capabilities across the organization.
This role requires both strategic thinking and hands-on expertise. You’ll be responsible for shaping the data science roadmap, mentoring a growing team, and collaborating with product, engineering, and business stakeholders to translate business challenges into practical machine learning solutions.
What you’ll do:
- Design, develop, and maintain backend services for AI agents and tool integrations using latest technologies
- Build scalable APIs and microservices that interface with LLMs and AI frameworks
- Implement agent orchestration systems, tool calling mechanisms, and workflow engines
- Optimize performance and reliability of AI-powered applications at scale
- Develop data pipelines for training, evaluation, and monitoring of AI systems
- Integrate with various LLM providers (OpenAI, Anthropic, etc.) and manage API interactions
What you’ll need:
- Excellent coding skills in Python/TypeScript, with at least 5 years of hands-on experience building reliable backend services, agents and tooling. Familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) is a strong advantage.
- Experience designing, deploying, and maintaining production systems that integrate ML components, including APIs, microservices, model serving layers, feature pipelines, monitoring, and CI/CD/MLOps workflows.
- Solid experience with AI related contexts Understanding of prompt engineering and LLM optimization techniques, RAG architecture
- Solid understanding of distributed systems concepts, performance optimization, observability, and operating services at scale.
- Strong communication skills, with the ability to bridge technical, product, and business perspectives.
- Prior experience in cybersecurity, fraud prevention, or identity management is a plus, especially with secure system architectures or ML-augmented decisioning systems.
Advantages
- Experience integrating with LLM APIs (OpenAI, Anthropic Claude, etc.)
- Experience with agent frameworks (LangChain, LlamaIndex, AutoGPT)
- Background in ML/AI concepts and model deployment
- Experience with message queues (RabbitMQ, Kafka) and event-driven architectures
- Experience with function calling and tool use patterns in LLMs
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