Note: The job is a remote job and is open to candidates in USA. Harnham is seeking a hands-on Lead Backend Engineer – AI Services to build next-generation AI-enabled platform capabilities in a rapidly growing SaaS environment. The role focuses on designing, building, deploying, and scaling backend services for AI-driven applications, retrieval systems, orchestration frameworks, and customer-facing capabilities while partnering with Engineering, Product, and Data Science teams.
Responsibilities
- Design and build scalable backend services that enable AI capabilities across the platform
- Develop production-grade infrastructure supporting LLM integrations, retrieval systems, orchestration layers, and AI-powered workflows
- Create highly reliable Java and Python-based backend services used across multiple products and customer environments
- Build reusable platform capabilities for model serving, RAG, vector search, AI agents, and workflow orchestration
- Partner closely with Data Science teams to move AI innovations from experimentation into production
- Architect reusable backend patterns, shared services, and platform frameworks that accelerate future AI development
- Influence backend strategy, technical direction, and platform architecture
- Continuously improve scalability, reliability, observability, security, and maintainability across production systems
Skills
- • Backend Engineering
- • Java
- • Spring Boot
- • Python
- • FastAPI
- • Microservices
- • REST APIs
- • Distributed systems
- • Event-driven architectures
- • AI / ML Integration
- • OpenAI, Anthropic, Gemini, Bedrock, Azure OpenAI, or similar platforms
- • Production AI-enabled applications
- • RAG architectures and retrieval systems
- • LangChain or similar orchestration frameworks
- • Vector databases
- • AI agents and agentic workflows
- • Prompt engineering and orchestration design
- • AI evaluation and monitoring systems
- • SQL
- • PostgreSQL
- • Data platforms
- • Analytics systems
- • Business intelligence platforms
- • Data integration services
- • Model serving infrastructure
- • ML pipelines and MLOps tooling
- • AWS, Azure, or GCP
- • Docker
- • Kubernetes
- • Linux
- • CI/CD
- • Infrastructure automation
- • Monitoring and observability platforms
- • Production operations
Company Overview
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