What You Will Do

ML & AI Systems

• Design, train, and own the full lifecycle of ML models for payment optimization — routing decisions, authorization rate improvement, cost reduction, and fraud signals — using PyTorch, TensorFlow, or XGBoost.

• Build and operate LLM-powered workflows: LangGraph agent orchestration, RAG pipelines, and vector DB integrations (Pinecone, pgvector, or Weaviate).

• Own the MLOps stack end-to-end: experiment tracking (MLflow / W&B), model registry, feature store, and automated retraining pipelines on AWS SageMaker.

• Monitor model health continuously — drift, distribution shifts, retraining triggers — and define evaluation metrics tied directly to business outcomes.

Platform Engineering & Payments Integration

• Build and maintain inference services in Go and Python integrated into live payment routing — strict latency SLAs (<100 ms), zero silent errors.

• Own AWS infrastructure: ECS/EKS, Terraform IaC, SQS/SNS event streaming, RDS/Aurora, and S3 for model artifacts.

• Design and ship on-premise and hybrid deployment architectures for enterprise clients requiring local data residency, including secure data sync pipelines.

• Apply PCI-DSS standards across all components touching payment data; implement tokenization in ML pipelines; design for PSP-specific behavior (Cybersource, Worldpay, Prosa, Cielo, Pagbank, and others).

• Build and maintain RESTful and gRPC APIs that expose AI platform capabilities to merchants and partners.

Technical Leadership

• Own observability end-to-end: Prometheus/Grafana dashboards, OpenTelemetry tracing, model-specific monitors, and on-call runbooks.

• Set the engineering bar for the team: architecture reviews, code standards, testing strategy (unit, integration, shadow mode), and CI/CD practices.

• Mentor engineers, run design reviews, and translate product vision into executable technical roadmaps with clear timelines and trade-offs.

Technical Skills

Backend / Platform

• Go (production services)

• Python (ML + tooling)

• gRPC & REST APIs

• Event streaming (SQS/SNS)

• Distributed systems

Cloud & Infra — AWS

• ECS / EKS

• Terraform / IaC

• SageMaker or Vertex AI

• RDS/Aurora, S3

• Hybrid / on-prem deploy

AI / ML Stack

• PyTorch or TensorFlow

• XGBoost / scikit-learn

• MLflow / W&B

• Feature stores

• Model monitoring & drift

LLMs & Agents

• LangGraph / LangChain

• RAG + vector DBs

• Prompt engineering

• LLM evaluation

• Structured outputs

Payments Domain

• PCI-DSS compliance

• Tokenization patterns

• PSP integrations

• Auth rate optimization

• Routing orchestration

Frontend

• React / Next.js

• TypeScript

• Component systems

• API integration

Observability

• Prometheus / Grafana

• OpenTelemetry

• Structured logging

• On-call runbooks

Data

• SQL (analytical)

• Airflow / dbt

• Feature pipelines

• Data quality & lineage

What We Are Looking For

• 8+ years in software engineering; 3+ at Staff, Principal, or Tech Lead level owning a production platform end-to-end.

• Proven track record shipping ML/AI systems to production: training, serving, monitoring, and retraining — not just prototyping.

• Hands-on LLM experience in production: agents, RAG pipelines, or AI workflow orchestration.

• Payments or fintech background with practical knowledge of PSP behavior, PCI-DSS scope, authorization logic, and routing trade-offs.

• Experience designing and deploying on-premise or hybrid enterprise infrastructure.

• Bachelor's degree in Computer Science, Engineering, or equivalent demonstrated depth.