AI Systems Engineer

About the role

As an AI Systems Engineer you will be the core builder of our generative AI and machine‑learning practice, turning LLM concepts into reliable production solutions for a variety of clients.

Key responsibilities

• Design and implement robust LLM applications, Retrieval‑Augmented Generation (RAG) systems and multi‑step agentic workflows tailored to client use cases.
• Create and maintain MLOps and orchestration pipelines (Airflow, Dagster, Kubeflow) that ingest data, trigger models and deliver outputs reliably.
• Assess when to use commercial APIs versus fine‑tuning or hosting open‑source models (Llama, Mistral) to meet privacy, latency and cost requirements.
• Containerize ML and LLM micro‑services with Docker/Kubernetes, expose them via secure FastAPI endpoints and manage deployments on AWS, Azure or GCP.
• Build configuration‑driven platforms that allow consultants to adjust prompts, logic and processing nodes without code changes.

Required profile

• 3‑5 years of software or data‑engineering experience with hands‑on production LLM deployments.
• Deep knowledge of Python and modern LLM frameworks such as LangChain, LlamaIndex and DSPy.
• Proven ability to build reliable ML pipelines using workflow orchestration, containerisation and monitoring tools.
• Experience with LLM fine‑tuning techniques (LoRA, PEFT) and hosting open‑weight models.
• Strong foundation in API design, Git, automated testing and system architecture.

Required skills

• Python
• LangChain
• LlamaIndex
• DSPy
• Vector databases
• Airflow
• Dagster
• Kubeflow
• Docker
• Kubernetes
• FastAPI
• AWS
• Azure
• GCP
• CI/CD pipelines
• Git
• Automated testing
• API design
• LLM fine‑tuning (LoRA, PEFT)