AI Technical Lead

Client Strategy Development: Guide clients through AI strategy formulation, helping them identify optimal AI applications needs.

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 and data engineers to deliver comprehensive AI solutions.

Workflow Integration: Design AI solutions that seamlessly integrate with existing the application workflow.

Compliance & Standards Leadership: Ensure all AI systems comply with relevant industry standards and best practices.

Domain Expertise Development: Collaborate closely with the client, and their experts to understand complex challenges and translate them into AI solutions.

AI Ethics and Safety: Implement explainable AI frameworks, bias mitigation strategies, and safety protocols specifically designed for applications.

Model Validation: Design and execute rigorous validation protocols for AI models in this setup, ensuring performance, quality, and adherence to standards.

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.

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.

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

Explainable AI: Proficiency in XAI techniques –interpretability for highly regulated applications is crucial

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