Backend Engineer for AI Production Systems

Mission.dev; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Mission.dev is seeking a software engineer to design and own production systems end-to-end, building scalable APIs, microservices, and data pipelines to operationalize ML models.

You will focus on high performance, fault tolerance, and observability, bridging backend engineering with ML deployment, and work with Python/Go/Java, SQL/NoSQL, and streaming systems.

Qualifications

• Production-grade backend experience.
• Design scalable APIs and microservices.
• Experience with ML deployment pipelines.
• Strong understanding of distributed systems.
• Authorization to work in the USA.

Responsibilities

• Design and maintain scalable APIs and microservices to support high-throughput production environments.
• Build robust data pipelines for model ingestion and processing using SQL and NoSQL databases.
• Deploy machine learning models via inference frameworks and serving patterns.
• Implement observability with logging, metrics, and alerting across the stack.
• Architect distributed systems focusing on low latency, fault tolerance, and messaging queues.
• Triage and debug ML models in production to ensure reliable performance.
• Manage embedding pipelines and integrate vector search capabilities to enhance application intelligence.

Skills

Python

Go

Java

Distributed systems

SQL & NoSQL

Observability

ML model integration

Tools

Kafka

Redis

SQL databases

NoSQL databases

Job description

Mission is a platform for hiring, vetting and managing software development talents. It enables our clients to connect with the world’s best talent to build mission-critical software products.

About the Company

This enterprise software company provides an AI platform designed to automate complex operations such as demand forecasting and supply chain optimization. The platform bridges the gap between experimental pilots and production-ready workflows, enabling organizations in global industries to deploy scalable AI solutions. By focusing on governed intelligence and data integration, the company helps businesses achieve operational efficiency through an interface for building and managing production-grade AI applications at scale.

About the Role

As a software engineer, you will design and own production systems end-to-end, focusing on the infrastructure required to operationalize machine learning models. You will be responsible for building the scalable APIs, microservices, and data pipelines that support reliable AI applications. Rather than focusing on model training, your impact lies in shipping the robust systems that surround these models, ensuring high performance, fault tolerance, and observability at scale. You will act as the primary bridge between core backend engineering and machine learning deployment.

What You'll Do

• Design and maintain scalable APIs and microservices to support high-throughput production environments.
• Build robust data pipelines for model ingestion and processing using SQL and NoSQL databases.
• Deploy machine learning models via specialized inference frameworks and serving patterns like batching and async inference.
• Implement observability across the stack, including comprehensive logging, metrics, and alerting systems.
• Architect distributed systems focusing on low latency, fault tolerance, and message queuing.
• Triage and debug machine learning models in production to ensure consistent performance and reliability.
• Manage embedding pipelines and integrate vector search capabilities to enhance application intelligence.

What You Bring

• Extensive experience building and maintaining production-grade backend systems in languages such as Python, Go, or Java.
• Strong understanding of system design principles, including distributed systems and data consistency.
• Technical proficiency with SQL and NoSQL databases, caching layers, and message brokers like Kafka or Redis.
• Proven experience shipping to production with built-in observability and monitoring.
• Practical knowledge of machine learning libraries such as PyTorch, scikit-learn, or HuggingFace for model integration and debugging.
• Based in the US with authorization to work in the USA, no visas or sponsorships.

Nice to Haves

• Experience with vector search engines and embedding pipelines.
• Knowledge of advanced model serving patterns and asynchronous inference.
• Prior experience in manufacturing, retail, or finance sectors.