Job Description

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

.