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).

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