Cbtw Ai Technical Lead

**Overview**:
**Responsibilities**:

- Design, develop, and optimize machine learning models for various use cases including classification, regression, NLP, and computer vision
- Build end-to-end data pipelines to support model training, validation, and deployment
- Implement MLOps practices for continuous integration, model versioning, and automated deployment
- Collaborate with cross-functional teams including data engineers, product owners, and business stakeholders to define problem statements and deliver actionable solutions
- Conduct experiments, analyze model performance, and iterate based on data-driven insights
- Ensure model interpretability, fairness, and robustness in production environments
- Document methodologies, findings, and share knowledge through internal collaboration platforms
- Technology Assessment: Evaluate and recommend appropriate AI technologies (traditional ML, LLMs, agent systems) based on client requirements and the use cases
- Project Leadership: Lead cross-functional teams including experts, data engineers, and regulatory specialists to deliver comprehensive AI solutions
- Regulatory Compliance Leadership: Ensure all AI systems comply with regulations including GDPR, HIPAA, FDA guidelines for AI devices, and other relevant AI standards
- Domain Expertise Development: Collaborate closely with the client, and their experts to understand complex challenges and translate them into AI solutions
- Validation: Design and execute rigorous validation protocols for AI models in this setup, ensuring safety, efficacy, and regulatory compliance

**Training & Coaching Responsibilities**:

- Mentor, coach, and guide team members in AI technical domains to strengthen expertise and ensure alignment with company standards. Develop technical guidelines, best practices, and reusable frameworks to drive consistency and quality across projects. Organize workshops, code reviews, and knowledge-sharing sessions to disseminate expertise in ML, LLMs, and AI systems.
- Support career development of AI engineers by providing structured feedback and growth opportunities in line with organizational goals.
- Ensure that training and coaching activities are aligned with the company’s long-term AI strategy and core values.

**Qualifications**:
**Large Language Models**:

- Hands-on experience with LLM implementation - in contexts is a plus-, including prompt engineering and agent development
- Experience with large-scale data processing frameworks (e.g., Spark, Dask)
- Background in working with NLP (e.g., transformers, LLMs), computer vision, or time-series data
- Strong programming skills in Python, with experience in machine learning libraries such as TensorFlow, PyTorch, and scikit-learn
- Hands-on experience deploying models on cloud platforms (AWS, GCP, or Azure)
- Familiarity with MLOps tools such as MLflow, Kubeflow, or Airflow
- Experience with Docker and Kubernetes for containerization and orchestration
- Proficiency in working with APIs and integrating AI models into production systems
- Master’s degree or PhD in Computer Science, Artificial Intelligence, Data Science, or a related field

**Skills that will give you an advantage**:

- Computer Vision: Expertise in movement analysis systems
- Stakeholder Management: Ability to communicate complex AI concepts to medical professionals, and non-technical stakeholders
- Regulatory Knowledge: Familiarity with FDA AI device guidelines, ISO 13485, IEC 62304, and data privacy regulations
- 5-8+ years of AI/ML experience.
- Experience leading technical teams and client-facing consulting engagements preferred.
- Excellent in English both written and verbal.