About the role
Requirements:
- 8+ years building and deploying production AI/ML systems with demonstrated emphasis on solving the underlying data problem, not just model selection or infrastructure.
- Deep experience working with complex, domain-specific data at scale: extracting signal from noisy real-world datasets, building training data from historical decision records, and handling high-cardinality label spaces.
- Deep expertise across multiple AI domains (NLP, traditional ML, Gen AI) with production deployments in each.
- Expert-level proficiency in PyTorch or TensorFlow; experience with model training at scale.
- Proven track record on AI/ML projects where data quality, labelling strategy, and training data curation were the primary challenge, not just model architecture or deployment.
- Experience with cloud-native AI architectures on AWS, Azure, or GCP; containerised deployments (Docker, Kubernetes) for AI workloads.
Preferred:
- Experience building self-improving AI systems: active learning pipelines, feedback loop architectures that connect outcome signals back to model updates, or reinforcement learning from human feedback (RLHF) / outcome-based reward signals.
- Data mining experience at scale across high-cardinality feature spaces, finding rare but high-value patterns across large taxonomy or code spaces.
- Hands-on experience with domain-specific AI systems that learn from historical decisions: e. g., autonomous decision engines, recommendation systems, or classification pipelines that adapt from labelled outcomes over time.
- Experience with MLOps tooling: CI/CD for ML, model versioning, monitoring, and deployment governance.
- Experience in healthcare technology, medical AI, or regulated industries, including HIPAA-compliant system design and audit trail requirements.