AI Engineer

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October 16

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Board of Innovation

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Artificial Intelligence • B2B • Enterprise

Board of Innovation is an AI innovation consultancy focused on autonomous innovation. They help businesses reinvent growth strategies and operating models for the autonomous age by designing AI-powered engines and providing strategic guidance on AI integration. Their goal is to unlock new levels of growth, efficiency, and innovation through their AI Strategy Guide and other resources. Board of Innovation partners with leaders to pioneer AI-native transformations in various sectors, ensuring businesses are well-equipped to thrive in a rapidly evolving technological landscape.

51 - 200 employees

Founded 2010

🤖 Artificial Intelligence

🤝 B2B

🏢 Enterprise

📋 Description

• Work together with a collaborative international team of engineers, designers and solution leads to understand customer business needs and translate them into end-to-end solutions. • Leverage AI for predictive analysis, automation, decision support, and operational optimization. • Leverage LLMs for content generation, entity extraction, analyzing and summarizing large amounts of textual data, LLM orchestration, and agentic workflows. • Wrap analytical models into deployable APIs. • Collaborate with the data engineering team to integrate data pipelines and software developers to turn your solutions into customer-facing applications.

🎯 Requirements

• 3+ years of experience building AI/ML algorithms and solutions, and a degree in Computer Science or related field. • Experience with Python, and with ML frameworks like PyTorch and scikit-learn. • A strong understanding of various machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and neural networks. • Hands-on experience with Natural Language Processing (NLP), traditional ML, and LLMs. • Experience with data preprocessing, feature engineering, and model evaluation techniques essential for machine learning projects. • Familiarity with the latest in GenAI, including RAG, agentic workflows, MCP, A2A, tool use, function calling, and multi-modal systems. • Experience with MLOps, including monitoring and optimizing AI models for performance, scalability, and efficiency. • Familiarity with DevOps practices, including CI/CD pipeline tools such as Docker, Kubernetes, Github Actions, or Skaffold.