AI Technical Lead

Job Requirements

At Quest Global, it's not just what we do but how and why we do it that makes us different. With over 25 years as an engineering services provider, we believe in the power of doing things differently to make the impossible possible. Our people are driven by the desire to make the world a better place—to make a positive difference that contributes to a brighter future. We bring together technologies and industries, alongside the contributions of diverse individuals who are empowered by an intentional workplace culture, to solve problems better and faster.

Developing AI / Gen AI Models And Algorithms

• Designing, building, and training machine learning and deep learning models.
• Implementing algorithms for tasks like natural language processing (NLP), computer vision, and predictive analytics.
• Integrating AI into Applications:
• Embedding AI functionalities into existing software systems and applications.
• Developing APIs and interfaces for AI-powered features.
• Data Handling and Processing:
• Working with large datasets, including data cleaning, preprocessing, and feature engineering.
• Ensuring data quality and integrity for AI model training.
• Collaboration and Communication:
• Working closely with data scientists, engineers, and stakeholders.
• Communicating technical concepts to non-technical audiences.
• Optimization and Deployment:
• Optimizing AI models for performance, scalability, and efficiency.
• Deploying AI solutions to production environments.
• Staying Current with AI Trends:
• Continuously learning and researching the latest AI technologies and advancements.
  
  

Managing AI Projects

• Manage and lead AI projects
• Guide, mentor, and upskill data scientists, ML engineers, and analysts
• Planning team capacity and allocating tasks based on strengths
  
  

Work Experience

• Strong expertise in AI/ML technologies including machine learning, deep learning, NLP, LLMs, and model evaluation techniques.
• Hands‑on experience with AI engineering tools such as Python, TensorFlow/PyTorch, cloud ML platforms (Azure, AWS, GCP), and MLOps frameworks.
• Proven ability to lead and mentor teams of data scientists, ML engineers, and developers, with a focus on coaching, reviews, and skill development.
• Experience managing end‑to‑end AI solution lifecycle—problem discovery, data preparation, model development, deployment, and monitoring.
• Strong understanding of data architecture including data pipelines, feature engineering, model governance, and data quality.
• Ability to translate business needs into AI use cases and communicate complex technical concepts to non‑technical stakeholders.
  
  

Benefits

NA