Lead Backend Engineer - AI Services

Harnham; Austin (TX); Hybrid; USD 190,000 - 260,000; Full time

Job summary

Harnham is seeking a Lead Backend Engineer to drive AI platforms and services. You will lead backend design for scalable, secure production systems that enable AI adoption across software products.

You will collaborate with data science and analytics teams to transition prototypes into customer-ready services, building reusable infrastructure and setting engineering standards for enterprise-grade solutions.

Qualifications

• 15+ years of experience designing, building, and maintaining production SaaS apps.
• Strong backend software engineering using Java and modern server-side practices.
• Experience designing scalable architectures for AI-enabled apps.
• Experience integrating machine learning and generative AI into software products.
• Ability to balance experimentation with reliability, security, and maintainability.
• Experience collaborating with distributed or remote teams.
• Working knowledge of SQL and data-oriented design.
• Experience with cloud platforms like AWS, Azure, or Google Cloud.
• Excellent troubleshooting, debugging, and documentation skills.
• Strong decision-making on architecture and build-vs-buy choices.

Responsibilities

• Design, develop, and enhance backend services for scalability and reliability.
• Lead AI-focused platform capabilities from discovery to production deployment.
• Collaborate with data science to turn prototypes into customer-facing services.
• Build infrastructure for AI use cases: model deployment, orchestration, retrieval.
• Create architectures that enable fast experimentation with stability.
• Evaluate emerging AI tech to support product innovation.
• Establish standards and reusable components for future work.
• Design data-centric platform solutions: services, APIs, pipelines, integrations.
• Contribute to monitoring, CI/CD, security, and production support.

Skills

Backend design

Java

Python

Distributed teams

AI/ML integration

Cloud platforms

Security practices

Testing & CI/CD

Linux production

Troubleshooting

Tools

LangChain

MLflow

Vector databases

RAG

LLM APIs

Model serving

Orchestration frameworks

Feature stores

Prompts engineering

Agent-based workflows

Job description

Lead Backend Engineer - AI Platforms & Services

Overview

A growing technology company is seeking a senior engineering leader to expand and modernise its backend platform while enabling the adoption of artificial intelligence across its software products. This role focuses on transforming experimental AI concepts into scalable, secure, and maintainable production services that support long‑term platform growth. The successful candidate will combine deep backend engineering expertise with experience delivering AI‑enabled applications. They will work closely with cross‑functional teams to develop reusable platform capabilities, establish engineering best practices, and drive complex technical initiatives from concept through deployment.

Key Responsibilities

• Design, develop, and enhance backend services that improve platform scalability, reliability, performance, and maintainability.
• Lead the implementation of AI‑focused platform capabilities, taking ownership of initiatives from discovery and solution design through production deployment and ongoing operations.
• Collaborate with data science and analytics teams to transition prototypes and experimental models into customer‑facing production services.
• Build reusable infrastructure and services supporting AI use cases, including model deployment, orchestration, retrieval systems, and external AI integrations.
• Create architectures that support rapid experimentation while maintaining operational stability and enterprise‑grade quality standards.
• Evaluate emerging AI technologies, frameworks, and deployment approaches to support product innovation.
• Establish engineering standards, architectural patterns, and reusable components that accelerate future development efforts.
• Design and support data‑centric platform solutions, including services, APIs, pipelines, integrations, and workflow automation.
• Contribute to platform monitoring, testing strategies, CI/CD processes, security practices, and production support.

Required Qualifications

• Authorised to work in the United States without employer sponsorship.
• Experience collaborating effectively within distributed or remote engineering teams.
• 15+ years of experience designing, building, and maintaining production SaaS applications.
• Strong backend software engineering experience using Java and modern server‑side development practices.
• Proven experience designing and operating scalable architectures for AI‑enabled applications.
• Experience integrating machine learning and generative AI capabilities into commercial software products.
• Demonstrated ability to navigate both prototype and production environments while balancing experimentation, scalability, reliability, security, and maintainability.
• Practical knowledge of:
  • Python
  • FastAPI
  • MLflow
  • LangChain or similar orchestration frameworks
  • Vector databases
  • Retrieval‑Augmented Generation (RAG)
  • LLM APIs
  • Model serving technologies
  • Feature stores
  • Machine learning pipelines
  • Prompt engineering
  • Agent‑based and agentic workflow patterns
• Working knowledge of SQL and data‑oriented system design.
• Experience building or supporting analytics, business intelligence, decision support, or data platform solutions.
• Strong software engineering fundamentals, including:
  • Automated testing
  • CI/CD
  • Monitoring and observability
  • Security practices
  • Source control management
• Experience deploying and supporting critical production systems in Linux environments.
• Experience working with cloud platforms such as AWS, Azure, or Google Cloud.
• Strong technical decision‑making skills related to architecture, technology selection, and build‑versus‑buy evaluations.
• Excellent troubleshooting, debugging, documentation, and communication skills.
• Ability to work independently on multiple complex initiatives within an established codebase.

Preferred Qualifications

• Experience building infrastructure and tooling that supports AI experimentation and model lifecycle management.
• Experience partnering closely with data science teams to accelerate research‑to‑production workflows.
• Background establishing reusable platform services that support multiple product teams and future scalability.