Quest Global
Solving the world’s hardest engineering challenges through end‑to‑end solutions across industries.
AI Technical Lead
Lead AI projects, design and deploy models, mentor team, and integrate AI solutions.
Job Highlights
About the Role
The position involves overseeing AI initiatives, mentoring data scientists, machine‑learning engineers, and analysts, and planning team capacity by aligning tasks with each member’s strengths. • Design, build, and train machine learning and deep learning models. • Implement algorithms for NLP, computer vision, and predictive analytics. • Embed AI functionalities into existing software systems. • Develop APIs and interfaces for AI-powered features. • Work with large datasets, including cleaning, preprocessing, and feature engineering. • Ensure data quality and integrity for AI model training. • Collaborate closely with data scientists, engineers, and stakeholders. • Communicate technical concepts to non‑technical audiences. • Optimize AI models for performance, scalability, and efficiency. • Deploy AI solutions to production environments. • Continuously learn and research latest AI technologies and advancements. • Manage and lead AI projects. • Guide, mentor, and upskill data scientists, ML engineers, and analysts. • Plan team capacity and allocate tasks based on strengths.
Key Responsibilities
- ▸model development
- ▸algorithm implementation
- ▸ai integration
- ▸api development
- ▸data engineering
- ▸team leadership
What You Bring
Candidates should have strong expertise in AI/ML technologies, including machine learning, deep learning, NLP, large language models, and model evaluation techniques. Hands‑on experience with Python, TensorFlow/PyTorch, cloud ML platforms (Azure, AWS, GCP), and MLOps frameworks is required. Proven ability to lead and mentor teams of data scientists, ML engineers, and developers, focusing on coaching, code reviews, and skill development. Experience managing the end‑to‑end AI solution lifecycle—from problem discovery and data preparation to model development, deployment, and monitoring—is essential. A solid understanding of data architecture, pipelines, feature engineering, model governance, and data quality, along with the capacity to translate business needs into AI use cases and communicate complex concepts to non‑technical stakeholders, is also expected.
Requirements
- ▸ai/ml
- ▸python
- ▸tensorflow
- ▸mlops
- ▸leadership
- ▸cloud
Work Environment
Hybrid