AI Engineer

Position Summary

You build AI applications that hold up in production - whether that's a classic predictive model, a computer vision pipeline, or an LLM-based feature - because you're not a single-tool specialist. At the Middle level, you build and evaluate ML/GenAI pipelines using an established architecture. At the Senior level, you also fine-tune and productionize models yourself, choose the right approach (classic ML vs. LLM) for the problem, design the model-serving and monitoring architecture, and are the one who diagnoses why a model's output quality degraded in the wild.

Key Responsibilities

• Design, train, and evaluate machine learning models - classification, regression, recommendation, or computer vision - depending on the problem.
• Develop and deploy GenAI applications using LLMs (OpenAI, Llama) and LangChain where an LLM is the right tool, not the default one.
• Build and evaluate Retrieval-Augmented Generation (RAG) pipelines with Vector Databases (Pinecone/Milvus) when needed.
• Fine-tune pre-trained models for specific domain tasks using PyTorch or TensorFlow.
• Implement MLOps practices for model deployment, monitoring, and versioning - not just a one-off notebook.
• Collaborate with backend engineers to expose AI capabilities via production-grade APIs.
• (Senior) Own the model-serving and monitoring architecture, choose the right modeling approach for ambiguous problems, and diagnose model-quality regressions in production.
  
  

Qualifications

• English: fluent with good verbal and written communication - reads dense model documentation and research papers, and explains technical trade-offs clearly to non-ML stakeholders.
• 2+ years (Middle) to 4+ years (Senior) of Software Engineering experience with a real focus on AI/ML, not just prompt engineering.
• Strong proficiency in Python and at least one deep learning framework (PyTorch or TensorFlow).
• Hands-on experience with at least one of: classic ML modeling, computer vision, or NLP/LLM integration and Vector Databases.
• Understanding of MLOps pipelines and cloud deployment (AWS SageMaker/Vertex AI).
• Solid foundation in Computer Science, Mathematics, or a quantitative field.
• Uses AI coding assistants for your own tooling and infra scripts, not only for the models you ship - AI-assisted engineering applies to this role too.
• (Senior) Track record of fine-tuning and productionizing models beyond calling a hosted API, across more than one type of ML problem.
  
  

About GTEMAS

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