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About the Role
SLR is seeking an AI Development Engineer to build AI systems that operate reliably in the real world. This role focuses on designing and implementing production-grade systems powered by large language models (LLMs), working hands-on across the full delivery lifecycle from concept to production. You will collaborate with product, engineering, and data teams to deliver intelligent applications built on modern AI infrastructure. We value practical builders who can design, implement, deploy, and operate real systems that deliver business value.
Responsibilities
- Design and implement production-grade systems powered by LLMs and modern AI frameworks.
- Develop applications using technologies such as OpenAI, Anthropic and other LLM APIs, LLM gateway, Vector databases, and Agent orchestration frameworks.
- Build and operate the infrastructure required to run reliable AI services, including API services, orchestration layers, retrieval pipelines, observability and monitoring, and scalable backend services.
- Design integration layers that enable models to interact with external systems, including API integrations, tool-use systems for agents, connectors to databases, SaaS tools, or internal platforms, and structured prompting and function-calling architectures.
- Move quickly from concept to working product.
- Write clean, maintainable backend code.
- Build testable services.
- Deploy systems in production environments.
- Iterate based on real user feedback.
- Collaborate with product managers, engineers, and designers to turn ideas into working solutions.
Requirements
- Strong backend engineering experience.
- Proficiency in Python (preferred) or TypeScript.
- Experience building REST APIs and backend services.
- Solid system design fundamentals.
- Debugging and production troubleshooting skills.
- Understand software development lifecycle.
- Experience building applications using large language models.
- Prompt engineering and structured prompting.
- Tool use and function calling.
- Retrieval-Augmented Generation (RAG) architectures.
- LLM evaluation and iterative improvement.
- Hands-on experience deploying production systems.
- Docker and containerization.
- Cloud platforms (AWS, GCP, or Azure).
- CI/CD pipelines.
- Scalable service architecture.
- Experience building and operating knowledge layers.
- Experience with Vector databases (e.g. Pinecone, Weaviate, pgvector).
- Document ingestion pipelines.
- Embedding workflows.
- Search and retrieval optimization.
- Prefer building working systems over discussing them.
- Move quickly while maintaining quality.
- Enjoy solving messy, real-world problems.
- Take ownership from prototype through to production.
- Stay curious about emerging AI capabilities.
- Comfortable learning quickly and shipping continuously.
Skills
- Python
- TypeScript
- REST APIs
- Backend services
- System design
- Debugging
- Production troubleshooting
- Software development lifecycle
- Large language models (LLMs)
- Prompt engineering
- Structured prompting
- Tool use
- Function calling
- Retrieval-Augmented Generation (RAG)
- LLM evaluation
- Docker
- Containerization
- AWS
- GCP
- Azure
- CI/CD
- Scalable service architecture
- Vector databases
- Document ingestion
- Embedding workflows
- Search optimization
- Retrieval optimization
- MCP architectures
- Tool-connected AI systems
- Agent frameworks
- Knowledge graph systems
- Streaming systems
- Event-driven systems
- Distributed systems design
- AI evaluation frameworks
Experience Level
- 2–5 years of experience in software engineering, AI engineering, or ML systems
About the Company
- You will help build real AI systems at a time when the AI stack is still rapidly evolving.
- This role offers meaningful ownership and autonomy, real engineering challenges, and the opportunity to shape how intelligent software is designed, built, and deployed across SLR.