AI Engineer

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).

What You Will Build

• 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

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, LLM gateway, vector databases, and 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
• MCP and Tool Integration Layers: Design integration layers that enable models to interact with external systems, such as:
• API integrations
• Tool‑use systems for agents
• Connectors to databases, SaaS tools, or internal platforms
• Structured prompting and function‑calling architectures
• Ship Production Code: Concept to working product, clean maintainable backend code, testable services, production deployment, and iteration based on 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 (or TypeScript), building REST APIs, solid system design fundamentals, debugging and production troubleshooting skills, understanding of the software development lifecycle.
• LLM Application Development: Experience building applications using large language models, prompt engineering, 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, and scalable service architecture.
• Data and Retrieval Systems: Experience building and operating knowledge layers, vector databases (Pinecone, Weaviate, pgvector), document ingestion pipelines, embedding workflows, search and retrieval optimization.

Nice to Have

• 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

• 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.

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.

Why Join SLR

• Meaningful ownership and autonomy.
• Real engineering challenges.
• Opportunity to shape how intelligent software is designed, built, and deployed across SLR.
• Focus on sustainability across 125 countries with over 4,500 employees working on global environmental projects.