Senior AI Developer

FlexTrade Systems is a provider of customized multi-asset execution and order management trading solutions for buy- and sell-side financial institutions. Through deep client partnerships with some of the world's largest, most complex and demanding capital markets firms, we develop the flexible tools, technology and innovation that deliver our clients a competitive edge. Our globally distributed engineering teams focus on adaptable technology and open architecture to develop highly sophisticated trading solutions that can automate and scale with your business strategies.

At FlexTrade, we hold our values close to heart, with pride and gratitude, as they guide us in everything that we do. We are dedicated to giving our clients a competitive edge, taking ownership of our responsibilities, being flexible to adapt to ever changing environment and technology, bringing integrity to ever interaction and we continue to improve, grow together and collaborate as one team. All of these while having Fun truly makes FlexTrade a wonderful place to work.

About You:

The Senior AI Developer will be responsible for designing, building, and integrating AI-driven systems into both our customer-facing products and internal business tools.

This role sits at the intersection of AI engineering, full-stack development, and systems integration—ideal for someone who can architect, deploy, and maintain AI-powered applications across diverse environments.

You’ll own the full lifecycle: contextual engineering, model orchestration, evaluation, observability, and seamless SaaS integration.

Responsibilities:

• Collaborate with product, engineering, and operations teams to discover and design opportunities for embedding AI components into existing applications and workflows.

• Develop and integrate AI components across both product platforms and internal applications.

• Develop and maintain AI platforms for internal teams.

• Design and maintain multi-tenant SaaS AI systems with scalable APIs, authentication, and tenant isolation.

• Build and deploy agentic workflows, contextual pipelines, and AI copilots to augment user and employee productivity.

• Implement and manage MCP (Model Context Protocol) servers for modular, composable AI service integration.

• Create and maintain evaluation frameworks and observability dashboards for monitoring AI model performance, reliability, and user experience.

• Collaborate with frontend and backend teams to deliver AI features that integrate seamlessly with web and desktop applications.

• Architect and deploy on AWS or GCP, using modern tools for automation, scalability, and cost optimization.

• Enhance internal processes using AI: developer productivity (code intelligence, documentation), support tools (LLM-based assistants), and analytics (data summarization, insights).

• Partner with DevOps, data engineering, and security teams to ensure robust, compliant, and monitored AI integrations.

• Advocate best practices in Contextual Engineering, MLOps, and AI observability within the organization.

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or Equivalent areas.

• 5+ years of experience in software development, with strong exposure to AI systems integration.

• Proven track record building multi-tenant SaaS applications with microservices and secure APIs.

• Expertise in:

  • Python or Go (backend development).

  • React/Next.js (frontend integration).

  • TensorFlow, PyTorch, OpenAI API, or Hugging Face Transformers (AI development).

• Hands-on experience on AWS or GCP

• Experience with Context Engineering, MCP servers, retrieval-augmented generation (RAG), and Agentic Workflows.

• Experience designing secure, observable, and scalable integrations between internal and external systems.

• Excellent collaboration and communication skills, with the ability to translate AI concepts into business and product outcomes.

Preferred Skills:

• Knowledge of vector databases and semantic search.

• Familiarity with event-driven architecture (Kafka, Pub/Sub) and data streaming.

• Exposure to MLOps, CI/CD, and infrastructure as code.