Director of AI Automation

Integral Creative Solutions

Posted 2mo ago

The employer’s full description could not be read from their board. This is a summary of the posting — follow the apply link for the original.

Design, develop, and deploy scalable AI/ML models and systems
Diagnose model performance issues, optimize inference times, and guide integration of AI features into core products
Own the AI product roadmap in collaboration with Product Managers, Data Scientists, and Backend Engineers
Lead cross-functional squads in an Agile environment to operationalize prototypes
Ensure high model accuracy, low latency, and robust operationalization
Manage multiple workstreams with a disciplined backlog, rigorous code quality standards, and reproducible experiments
Establish and promote engineering best practices (CI/CD, testing, instrumentation, observability) across AI initiatives
Build, coach, and grow a high-performing AI/ML engineering team
Foster a culture of learning, experimentation, and psychological safety
Promote best practices for remote collaboration, knowledge sharing, and career development
Ensure compliance with data handling, privacy, and security requirements
Address regulatory considerations relevant to client industries
Drive responsible AI practices, including bias monitoring, interpretability, and auditability

10+ years of professional experience in Software Engineering, Data Science, or Machine Learning in production environments
Prior leadership or management experience overseeing AI/ML programs or teams
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Mathematics, or a related field (or equivalent practical experience)
Proficiency with Python and modern ML frameworks (PyTorch, TensorFlow, Keras, scikit-learn)
Familiarity with MLOps tools (MLflow, Kubeflow, Weights & Biases) and cloud ML services
Experience with deployment and monitoring of AI systems in production
Excellent written and verbal communication; ability to translate complex mathematical concepts into actionable business insights for non-technical stakeholders
Strong analytical mindset; ability to diagnose root causes of model failures and to drive lasting improvements
Solid grounding in software engineering principles (CI/CD, Git, Docker, unit/integration testing)
Ability to deliver clean, maintainable, scalable code and systems
Proven capability to work effectively in a distributed, asynchronous environment; self-motivated, disciplined, and communicative
Calm under pressure when experiments fail; pivot strategies quickly and convert setbacks into learning opportunities

AWS Certified Machine Learning – Specialty
Google Professional Machine Learning Engineer
DeepLearning.AI specializations
Experience with LLMs
Experience with Retrieval-Augmented Generation (RAG)
Experience with vector databases (e.g., Pinecone, Milvus)
Experience with reinforcement learning
Prior AI applications in FinTech, Healthcare, E-commerce, SaaS, or other relevant sectors
Technical blog writing
Open-source contributions
Conference presentations (e.g., NeurIPS, ICML, CVPR)
Experience managing GPU clusters
Experience with serverless inference
Experience with multi-cloud deployments (AWS, Azure, GCP)
Familiarity with regulated environments (HIPAA, SOC 2)
Applying differential privacy or federated learning techniques

Skills