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Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformJavaKubernetesPythonPyTorchTensorflowC++
About the role
Key responsibilities & impact- Design, develop and maintain production AI solutions, including LLM-based applications, retrieval-augmented generation (RAG) pipelines, agentic workflows and ML model services
- Take models and prototypes from applied scientists and turn them into robust, low-latency, cost-efficient services that scale to global traffic
- Define and automate evaluation frameworks, benchmarks and guardrails measuring accuracy, safety, latency and cost
- Monitor models once they are in production
- Build and optimize data pipelines for training, fine-tuning, embedding and inference
- Ensure data is high quality, traceable and handled in line with privacy requirements
- Implement CI/CD for ML, model versioning, experiment tracking and observability on cloud platforms
- Collaborate with product, engineering and science stakeholders to understand requirements, weigh trade-offs and deliver AI solutions meeting customer needs
- Evaluate new models, tools and techniques and share learnings with the wider engineering community
Requirements
What you’ll need- Bachelor's degree in Computer Science, Engineering, AI/ML or a related field, or equivalent professional experience
- Strong proficiency in one or more high-level programming languages such as C++, Java, Python, or similar languages
- Hands-on experience with LLMs and their ecosystem: prompt engineering, RAG, embeddings and vector databases, tool use and agent frameworks; fine-tuning is preferred
- Solid understanding of software architecture, API design, design patterns and best practices for maintainable, scalable systems
- Experience with cloud service providers such as Azure, AWS, or GCP
- Experience with containerization using Docker and Kubernetes
- Experience with CI/CD tools
- Knowledge of version control systems, preferably Git
- Excellent problem-solving and communication skills, with the ability to work effectively in a cross-functional team
- Experience with ML frameworks and libraries such as PyTorch, TensorFlow or Hugging Face is preferred
- Familiarity with MLOps practices and tools such as MLflow, experiment tracking, and model monitoring is preferred
- Experience with geospatial, mapping or location data is a plus, but not required
Benefits
Comp & perks- Dynamic team and vibrant culture
- Opportunity to contribute to shaping the future of location technology