Backend AI Engineer

About the role

We're looking for a Backend Engineer specializing in AI Integration to bridge the gap between our Data Science team and our B2B SaaS platform. You'll be responsible for taking ML models and LLM solutions from concept to production, transforming proof-of-concepts into robust, scalable features.

The challenge: Reduce AI feature deployment time from weeks to 2-3 days while maintaining production quality.

Key responsabilities

Integrate ML Models into Production

• Industrialize Data Scientists' models for production environments
• Build feature engineering pipelines from our database
• Create API serving infrastructure for predictions
• Monitor model performance and drift

Integrate Platform Features

• Develop robust API endpoints using FastAPI
• Write and optimize complex SQL queries (PostgreSQL)
• Integrate authentication, permissions, and logging systems
• Maintain and enhance existing Volta codebase conventions

Collaborate with Product & Data Teams

• Review and refine POCs generated with AI tools
• Challenge business specifications on edge cases
• Balance technical quality with business velocity

Maintain MLOps Infrastructure

• Implement model versioning strategies
• Monitor latency, errors, and operational costs
• Build A/B testing frameworks
• Execute rollback procedures when needed

Technical requirements

Backend: Python 3.11+, FastAPI, PostgreSQL, Redis, Docker, GitLab CI/CD

AI/ML: Experience with LLM APIs (Claude, GPT-4), ML model serving, familiarity with MLflow or Weights & Biases is a plus

Observability: Prometheus/DataDog, structured logging, Sentry

Cloud: AWS or GCP experience is a bonus

What we're looking for

Must Have:

• 3-5+ years production Python backend experience (FastAPI, Flask, Django)
• Strong PostgreSQL and SQL optimization skills
• At least one AI/ML project deployed to production
• Understanding of ML fundamentals (features, training/inference, model drift)
• Ability to collaborate with Data Scientists, PMs, and Tech Leads - adapting communication to each audience
• Collaborative autonomy: self-sufficient day-to-day, sync on key decisions
• Hands-on mindset and pragmatic approach: ship and iterate over perfection
• End-to-end ownership from conception to production

Important:

• Docker, CI/CD, testing (pytest), monitoring and observability
• Understanding of B2B business logic
• Clear communication across technical and non-technical profiles

Nice to Have:

• MLOps experience, data engineering background, B2B SaaS experience
• LangChain, Ray Serve, MLflow familiarity
• Open source AI/ML contributions

Ideal background

You're a Backend Engineer with strong AI/ML interest or an ML Engineer with extensive integration experience. You've integrated at least one ML model into production, collaborated with Data Scientists to translate notebooks into production code, and optimized backend performance at scale.

How to apply

Send your resume and briefly describe an AI/ML project you've deployed to production - what made it challenging and how did you solve it?

Apply here https://airtable.com/appUu1PfWgTfWxAqR/pagdqjPAnYfZtYbIr/form