Build Action-Oriented Agent Workflows: Design multi-step orchestration, tool use, and retrieval grounded in our data stores using the Strands Agents SDK.

Architect End-to-End Features: Own development across a React/TypeScript frontend and Python/FastAPI backend, balancing rapid iteration with platform maintainability.

Own the Reasoning Layer: Extend our Chain-of-Thought UI to ensure explainability. In a regulated environment, showing why an agent made a decision is a core feature, not a nicety.

Make Agents Measurably Reliable: Build and extend our evaluation harness (Ragas) and observability platform (Langfuse, with session continuity) so we ship changes based on evidence, not vibes.

Define Evaluation Strategies: Build custom evaluators, datasets, benchmarks, and automated regression suites to catch quality regressions before they hit production.

Integrate Systems & Clouds: Connect internal APIs, data warehouses, email, and cross-cloud GCP/AWS services with robust error handling and distributed tracing.

Industrialize Prototypes: Drive the discover → industrialize → productionize lifecycle, turning promising AI prototypes into hardened, daily-deployed services on CD pipelines.

Mentor & Lead: Uplift mid-level engineers through code reviews, pair programming, and establishing scalable engineering patterns.

Collaborate Cross-Functionally: Partner with Product, Ops, Trading, and Treasury to identify high-value workflows worth automating (and ruthlessly deprioritize the ones that aren't).

React, TypeScript

Micro-frontend architecture

Chain-of-Thought / Agent-reasoning UI

Vite, Vitest

Python, FastAPI

Strands Agents SDK (Agent Orchestration)

AWS Bedrock

RAG / Advanced Retrieval

Ragas (Evaluation), Langfuse (LLM Observability)

AWS (ECS Fargate, Lambda, API Gateway, S3) with cross-cloud GCP integration

Terraform, GitHub Actions

Docker

Event-driven integration patterns

5+ years of professional software engineering experience.

Strong Python Backend Expertise: Deep experience with FastAPI (or Django/Flask), asynchronous programming, clean architecture, and writing production-grade code.

Strong React & TypeScript Skills: Ability to architect non-trivial frontends with sound state management, performance optimization, and comprehensive testing.

System Design Judgment: A proven track record of designing scalable, maintainable systems and clearly articulating architectural trade-offs.

API & Integration Skills: Proficiency with REST, streaming (SSE/WebSockets), and the real-world complexities of stitching together messy upstream systems.

Data Proficiency: Strong SQL/NoSQL skills, query optimization, and data modeling.

Testing & Ownership Mindset: You treat testing as a core part of the development lifecycle and proactively drive features from ambiguous ideas to production.

Excellent Communication: Ability to explain technical decisions to both engineers and non-technical stakeholders, alongside a passion for writing useful documentation.

Agentic AI Experience: Hands-on experience with agent orchestration (Strands, LangGraph, AutoGen, or similar), RAG, tool use, and context engineering.

Production LLM Systems: Familiarity with LLM observability (Langfuse), evaluation (Ragas), token/cost management, and reliability patterns.

AWS Depth: Experience with Lambda, ECS, Bedrock, or equivalent cloud services (cross-cloud exposure is a major plus).

Event-Driven Architectures: Experience with event streaming and message brokers.

Regulated Environments: Experience working within fintech, compliance, or similar sectors with strict data-residency and auditability constraints.

Micro-frontends & IaC: Experience with micro-frontend architecture and Infrastructure as Code (Terraform/CDK).

Build Action-Oriented Agent Workflows: Design multi-step orchestration, tool use, and retrieval grounded in our data stores using the Strands Agents SDK.

Architect End-to-End Features: Own development across a React/TypeScript frontend and Python/FastAPI backend, balancing rapid iteration with platform maintainability.

Own the Reasoning Layer: Extend our Chain-of-Thought UI to ensure explainability. In a regulated environment, showing why an agent made a decision is a core feature, not a nicety.

Make Agents Measurably Reliable: Build and extend our evaluation harness (Ragas) and observability platform (Langfuse, with session continuity) so we ship changes based on evidence, not vibes.

Define Evaluation Strategies: Build custom evaluators, datasets, benchmarks, and automated regression suites to catch quality regressions before they hit production.

Integrate Systems & Clouds: Connect internal APIs, data warehouses, email, and cross-cloud GCP/AWS services with robust error handling and distributed tracing.

Industrialize Prototypes: Drive the discover → industrialize → productionize lifecycle, turning promising AI prototypes into hardened, daily-deployed services on CD pipelines.

Mentor & Lead: Uplift mid-level engineers through code reviews, pair programming, and establishing scalable engineering patterns.

Collaborate Cross-Functionally: Partner with Product, Ops, Trading, and Treasury to identify high-value workflows worth automating (and ruthlessly deprioritize the ones that aren't).

React, TypeScript

Micro-frontend architecture

Chain-of-Thought / Agent-reasoning UI

Vite, Vitest

Python, FastAPI

Strands Agents SDK (Agent Orchestration)

AWS Bedrock

RAG / Advanced Retrieval

Ragas (Evaluation), Langfuse (LLM Observability)

AWS (ECS Fargate, Lambda, API Gateway, S3) with cross-cloud GCP integration

Terraform, GitHub Actions

Docker

Event-driven integration patterns

5+ years of professional software engineering experience.

Strong Python Backend Expertise: Deep experience with FastAPI (or Django/Flask), asynchronous programming, clean architecture, and writing production-grade code.

Strong React & TypeScript Skills: Ability to architect non-trivial frontends with sound state management, performance optimization, and comprehensive testing.

System Design Judgment: A proven track record of designing scalable, maintainable systems and clearly articulating architectural trade-offs.

API & Integration Skills: Proficiency with REST, streaming (SSE/WebSockets), and the real-world complexities of stitching together messy upstream systems.

Data Proficiency: Strong SQL/NoSQL skills, query optimization, and data modeling.

Testing & Ownership Mindset: You treat testing as a core part of the development lifecycle and proactively drive features from ambiguous ideas to production.

Excellent Communication: Ability to explain technical decisions to both engineers and non-technical stakeholders, alongside a passion for writing useful documentation.

Agentic AI Experience: Hands-on experience with agent orchestration (Strands, LangGraph, AutoGen, or similar), RAG, tool use, and context engineering.

Production LLM Systems: Familiarity with LLM observability (Langfuse), evaluation (Ragas), token/cost management, and reliability patterns.

AWS Depth: Experience with Lambda, ECS, Bedrock, or equivalent cloud services (cross-cloud exposure is a major plus).

Event-Driven Architectures: Experience with event streaming and message brokers.

Regulated Environments: Experience working within fintech, compliance, or similar sectors with strict data-residency and auditability constraints.

Micro-frontends & IaC: Experience with micro-frontend architecture and Infrastructure as Code (Terraform/CDK).