Responsibilities:
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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.
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Productionize models: Take models and prototypes from applied scientists and turn them into robust, low-latency, cost-efficient services that scale to global traffic.
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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.
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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.
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MLOps and infrastructure: Implement CI/CD for ML, model versioning, experiment tracking and observability on cloud platforms.
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Cross-functional collaboration: Work with product, engineering and science stakeholders to understand requirements, weigh trade-offs and deliver AI solutions that meet customer needs.
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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.
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Strong proficiency in one or more high-level programming languages such as C++, Java, Python, or similar languages.
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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.
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Solid understanding of software architecture, API design, design patterns and best practices for maintainable, scalable systems.
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Experience with cloud service providers (e.g., Azure, AWS, GCP), containerization (Docker, Kubernetes) and CI/CD tools.
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Knowledge of version control systems, preferably Git.
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Excellent problem-solving and communication skills, with the ability to work effectively in a cross-functional team.
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Experience with ML frameworks and libraries such as PyTorch, TensorFlow or Hugging Face is preferred.
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Familiarity with MLOps practices and tools (e.g., MLflow, experiment tracking, model monitoring) is preferred.
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Experience with geospatial, mapping or location data is a plus, but not required.