Job Description
Are you ready to architect the technological landscape of tomorrow? Veridian Future Labs is seeking a visionary Senior AI/ML Engineer to lead our strategic roadmap toward 2026 and beyond. In this pivotal role, you will bridge the gap between theoretical research and practical application, ensuring our AI infrastructure is scalable, ethical, and future-proof.
We are not just building software; we are defining the standard for intelligent systems. Join a team of elite engineers and data scientists dedicated to pushing the boundaries of what is possible in the AI sector.
Why Join Veridian Future Labs?
- Work on cutting-edge generative AI and large language model architectures.
- Competitive equity package and performance bonuses.
- Flexible work environment with a focus on work-life balance.
- Opportunity to mentor junior talent and shape company culture.
Responsibilities
- Architect AI Solutions: Design, develop, and deploy scalable machine learning models and infrastructure pipelines using Python, TensorFlow, and PyTorch.
- Strategic Roadmap: Define the technical vision for 2026, identifying emerging trends in NLP, Computer Vision, and Autonomous Systems.
- Data Governance: Establish rigorous data quality standards and ensure ethical AI practices are integrated into every layer of the product stack.
- System Optimization: Continuously optimize model performance, reduce latency, and improve inference speeds for real-time applications.
- Cross-Functional Leadership: Collaborate with product managers, designers, and engineering teams to translate complex requirements into technical specifications.
- Research & Innovation: Stay at the forefront of industry advancements, conducting experiments and publishing research where applicable.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field.
- Experience: Minimum of 5+ years of professional experience in machine learning engineering or data science.
- Technical Skills: Proficiency in Python, SQL, and deep learning frameworks (TensorFlow, PyTorch, Keras).
- Model Deployment: Strong experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (Docker, Kubernetes, MLflow).
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders.
- Problem Solving: Proven track record of solving complex, ambiguous problems in high-pressure environments.