- Company Name
- WGSN
- Job Title
- AI Engineer
- Job Description
-
Job Title: AI Engineer
Role Summary: Full‑Stack AI Engineer responsible for taking large language model (LLM) and multimodal AI prototypes into production, designing APIs, CI/CD pipelines, inference infrastructure, and monitoring systems to power consumer‑trend forecasting products.
Expectations: Deliver scalable, secure, high‑performance AI services; convert notebooks into production‑ready components; collaborate closely with data scientists, data engineers, product managers, and platform teams; uphold AI governance and safety standards; continuously adopt emerging AI and MLOps technologies.
Key Responsibilities:
- Build, deploy, and maintain LLM/ multimodal models for production use.
- Convert experimental notebooks into robust, testable services.
- Design and implement retrieval‑augmented generation (RAG) systems, semantic search pipelines, embeddings, and vector search infrastructure.
- Develop CI/CD pipelines for model deployment, versioning, evaluation, rollback.
- Construct scalable inference infrastructure using Docker, Kubernetes, and AWS services (Lambda, ECS/EKS, API Gateway, S3, CloudWatch).
- Implement monitoring for latency, throughput, drift, hallucinations, quality, reliability, and cost.
- Design and develop production‑grade APIs and microservices (Python/FastAPI).
- Optimize inference performance via batching, caching, autoscaling, and efficient resource usage.
- Ensure all AI services meet reliability, scalability, performance, and security requirements.
- Collaborate with Data Engineering and DataOps to maintain high‑quality training and inference data pipelines.
- Build automated evaluation pipelines, test suites, and guardrails for safe, predictable behavior.
- Contribute to AI governance, safety, compliance, and responsible‑AI frameworks.
- Communicate technical concepts clearly to non‑technical stakeholders.
Required Skills:
- Proficient in Python and building production APIs (FastAPI or equivalent).
- Hands‑on experience deploying and operating LLM‑based systems at scale.
- Practical knowledge of RAG architectures, embeddings, vector databases, and semantic search.
- Strong familiarity with AWS services (Lambda, ECS/EKS, S3, API Gateway, CloudWatch).
- Solid understanding of CI/CD, Docker, Kubernetes, and related orchestration.
- Experience designing scalable, reliable, secure back‑end systems.
- Excellent problem‑solving and prototyping‑to‑production skills.
- Effective collaboration with cross‑functional teams.
- Ability to explain complex technical ideas to non‑technical audiences.
Required Education & Certifications:
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Engineering, or related field.
- Relevant certifications (e.g., AWS Certified Solutions Architect, AWS Certified DevOps Engineer, or similar ML engineering credentials) preferred.