AI Platform Tech Lead

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.