- Company Name
- Emeria
- Job Title
- AI Engineer
- Job Description
-
Job Title: AI Engineer
Role Summary:
Design, develop, and industrialise AI solutions with a strong focus on large language models (LLM), cloud-native architectures, and production-ready AI products. Drive the end-to-end lifecycle from concept to scalable, secure deployments on AWS, ensuring high performance, reliability, and cost-effectiveness.
Expectations:
- Deliver robust, scalable, and secure AI services that meet real‑world business needs.
- Maintain and evolve the Reemia AI platform, contributing to technical roadmap decisions.
- Own incident resolution, continuous improvement, and quality assurance, while upholding data governance and security standards.
- Collaborate cross‑functionally, share best practices, and foster a culture of experimentation balanced with industrialisation.
Key Responsibilities:
1. Conceptualise and engineer AI solutions, including LLM‑based applications and advanced pipelines (RAG, agents, prompt orchestration).
2. Build production‑grade Python code, implement micro‑services, and design distributed architectures.
3. Deploy and operate on AWS (Lambda, ECS/EKS, S3, IAM), ensuring scalability, monitoring, and cost optimisation.
4. Create reliable CI/CD pipelines, automated tests, observability, and documentation.
5. Monitor performance, troubleshoot incidents, and iteratively improve model robustness and feature set.
6. Enforce data security, compliance, and governance across data pipelines and model artefacts.
7. Mentor teammates, disseminate engineering best practices, and contribute to product‑driven decision making.
Required Skills:
- Expert in Python programming.
- Hands‑on experience with LLM frameworks (e.g., LangChain or equivalents), orchestration of agents, and API‑driven micro‑services.
- Proficiency with AWS services (Lambda, ECS/EKS, S3, IAM, CloudWatch, X-Ray).
- Strong software engineering fundamentals: clean code, unit/integration testing, performance optimisation, security hardening.
- Git proficiency, CI/CD tooling, version control best practices.
- (Preferred) Experience with vector or analytical databases, MLOps/LLMOps pipelines, and data‑governance frameworks.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related technical discipline.
- AWS Certified Solutions Architect or Developer certification is a plus.