As an AI Engineer, you will:
- Play a central role in advancing AI capabilities across the company and turning emerging AI technologies into practical, production-ready solutions
- Research and evaluate new AI technologies, identify valuable use cases and develop solutions that improve internal workflows, existing products and enable new AI-powered capabilities
- Lead AI solutions end to end, from research and proof of concept to design, development and production
- Collaborate closely with development, product and business teams to integrate AI models, agents, data and reusable AI services into existing systems
- Design and develop RAG, knowledge systems, AI agents and multi-agent workflows
- Evaluate and benchmark AI models and solutions, balancing quality, accuracy, latency, throughput and cost
- Find creative and practical solutions to complex technical and business challenges and help expand the company’s internal AI ecosystem
If you have:
- At least 3 years of experience in software engineering, machine learning engineering, applied AI or a related technical role
- Strong Python programming skills and experience building production-grade software, APIs and microservices
- Hands-on experience developing AI-powered applications and taking them from proof of concept to production
- Strong understanding of LLMs and transformer architectures
- Experience with RAG, embeddings and vector databases
- Experience in developing AI agents, tool-using workflows, MCP integrations or similar technologies
- Experience in evaluating and benchmarking AI models and solutions
- Familiarity with model-serving technologies such as vLLM, LiteLLM, SGLang, TGI or Triton
- Working knowledge of Docker, Kubernetes, Helm, Linux, Git, CI/CD and GitOps
It would be great if you also have:
- Experience with PyTorch, Hugging Face Transformers or similar frameworks
- Experience with fine-tuning techniques such as LoRA, QLoRA, SFT, preference optimization or distillation
- Experience with GraphRAG, knowledge graphs, code graphs or other graph-based AI systems
- Experience optimizing model inference through quantization, batching, caching, parallelism or GPU-aware deployment
- Experience with multimodal models, synthetic data generation or AI model evaluation
- Experience delivering AI solutions in security-sensitive, regulated, on-premises or disconnected environments
- B.Sc. in Computer Science or equivalent experience