A
AI Engineer
Job Description
What You'll Do
- Build, train and deploy AI models and algorithms that solve real business problems
- Work with product, engineering and business teams to bring AI into live systems and workflows
- Explore the latest in AI and machine learning, and turn what you learn into better products
- Tune models for speed, scale and accuracy
- Dig into large datasets to find insights that guide decisions
- Document your models, processes and workflows so the whole team can build on them
- Join code reviews, testing and debugging to keep quality high
- Help shape and grow our AI engineering team
- Bachelor's or Master's degree in Computer Science, AI, Machine Learning or a related field
- Around 2 years of experience deploying AI or ML models in a professional setting. Fresh graduates with relevant projects or internships are also welcome to apply
- Strong Python or Java skills, and experience with TensorFlow, PyTorch or Scikit-learn
- Familiarity with LLMs, AI agents, multi-model orchestration and application engineering
- Solid grounding in data structures, algorithms and software engineering
- Hands-on experience deploying models on AWS, Azure or Google Cloud
- Comfort working with large datasets and data preprocessing
- Strong problem-solving skills, whether you work independently or in a team
- Clear communication, including explaining technical ideas to non-technical colleagues
- Work on practical AI problems with real impact
- Learn and grow in a supportive, inclusive team
- Room to contribute ideas and shape how the team works
More Info
Key Skills
About Company
Skills:
React, Docker, Microsoft Azure, Flutter, Azure DevOps, LLMs, RAG, CI/CD, REST API design, prompt engineering
Skills:
Git, Python, Azure DevOps, LangGraph, LangChain, MCP servers, AI agents, Microsoft Agent Framework, Copilot Studio
Skills:
Tensorflow, Pytorch, Computer Vision, Natural Language Processing, Python, Speech Recognition, AI and machine learning
Skills:
Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Databases, Computer Vision, Version Control, Testing, Api Integration, Python backend development, software development practices
Skills:
Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), MLops, Docker, Distributed Systems, FastAPI, Helm, Python, Kubernetes, LangChain, Asynchronous processing, GitOps, LLMOps, AI Assisted Software Development Platforms, Workflow orchestration, LangGraph, Argo CD, Message-queuing technologies, CI/CD pipelines, Event-driven architectures