Senior AI Engineer

Responsibilities:

Build AI systems: Design, develop and maintain production AI solutions, including LLM-based applications, retrieval-augmented generation (RAG) pipelines, agentic workflows and ML model services.

Productionize models: Take models and prototypes from applied scientists and turn them into robust, low-latency, cost-efficient services that scale to global traffic.

Evaluation and quality: Define and automate evaluation frameworks, benchmarks and guardrails that measure accuracy, safety, latency and cost, and monitor models once they're in production.

Data and pipelines: Build and optimize data pipelines for training, fine-tuning, embedding and inference, making sure data is high quality, traceable and handled in line with privacy requirements.

MLOps and infrastructure: Implement CI/CD for ML, model versioning, experiment tracking and observability on cloud platforms.

Cross-functional collaboration: Work with product, engineering and science stakeholders to understand requirements, weigh trade-offs and deliver AI solutions that meet customer needs.

Continuous improvement: Keep up with the fast-moving AI ecosystem, evaluate new models, tools and techniques, and share what you learn with the wider engineering community.

Qualifications

• 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, and 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 (e.g., Azure, AWS, GCP), containerization (Docker, Kubernetes) and 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 (e.g., MLflow, experiment tracking, model monitoring) is preferred.

• Experience with geospatial, mapping or location data is a plus, but not required.