About the Role
SLR is seeking an AI Development Engineer who enjoys building AI systems that operate reliably in the real world. This role sits at the intersection of AI engineering, software development, and infrastructure, focusing on designing and implementing production-grade systems powered by large language models (LLMs).
You will work hands-on across the full delivery lifecycle—moving quickly from concept to prototype to production. Working closely with product, engineering, and data teams, you will help deliver intelligent applications built on modern AI infrastructure.
We value practical builders over academic theory. Success in this role is defined by your ability to design, implement, deploy, and operate real systems that deliver business value.
What You Will Build
You will design and implement systems across the AI stack, including:
LLM-powered applications and intelligent agents
Model orchestration and tool-use frameworks
Retrieval systems and knowledge layers (RAG)
MCP-style integration layers connecting models to tools, APIs, and data sources
Scalable infrastructure supporting AI workloads
Your work will progress rapidly from prototype to production, with real users and real constraints.
Key Responsibilities
Build AI Systems
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
Agent orchestration frameworks
Implement AI Infrastructure
Build and operate the infrastructure required to run reliable AI services, including:
API services supporting AI applications
Orchestration layers between models and tools
Retrieval pipelines and knowledge indexing
Observability and monitoring for AI systems
Scalable backend services
Develop MCP and Tool Integration Layers
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
Structured prompting and function-calling architectures
Ship Production Code
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 Across Teams
Work closely with product managers, engineers, and designers to turn ideas into working solutions
Required Skills
Software Engineering Foundations
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
LLM Application Development
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
Infrastructure and Deployment
Hands-on experience deploying production systems
Docker and containerization
Cloud platforms (AWS, GCP, or Azure)
CI/CD pipelines
Scalable service architecture
Data and Retrieval Systems
Experience building and operating knowledge layers
Vector databases (e.g. Pinecone, Weaviate, pgvector)
Document ingestion pipelines
Embedding workflows
Search and retrieval optimization
Nice to Have Experience with:
MCP architectures or tool-connected AI systems
Agent frameworks
Knowledge graph systems
Streaming or event-driven systems
Distributed systems design
Evaluation frameworks for AI systems
What we look for, we are looking for engineers who:
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
You do not need to know everything—but you should be comfortable learning quickly and shipping continuously.
Experience
2–5 years of experience in software engineering, AI engineering, or ML systems
We value evidence of building, including:
Shipped products
Real systems running in production
Open-source contributions
Side projects and experimentation
Demonstrated delivery matters more than credentials.
Why Join SLR
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
The opportunity to shape how intelligent software is designed, built, and deployed across SLR
