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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