Job Description
Are you ready to architect the future of intelligent systems? OmniFuture Systems is seeking a visionary Senior AI & Machine Learning Engineer to lead our research and development division. As we expand our footprint into the next generation of generative AI, we need a leader who can bridge the gap between theoretical breakthroughs and scalable production environments.
In this pivotal role, you will define the technical strategy for our 2026 roadmap, ensuring our platforms remain at the forefront of innovation. You will work closely with cross-functional teams of data scientists, engineers, and product managers to build AI solutions that are not only powerful but also ethical and efficient.
Why join us?
- Work on cutting-edge LLMs and generative models.
- Competitive salary and equity package.
- Flexible remote/hybrid work culture.
Responsibilities
- Lead Model Architecture: Design, implement, and optimize complex machine learning models, including Large Language Models (LLMs) and computer vision systems, tailored for high-volume production environments.
- Technical Strategy: Define and execute the technical roadmap for 2026, identifying emerging technologies and integrating them into our core product suite.
- Scalability & Performance: Oversee the deployment of models on cloud infrastructure (AWS/GCP) ensuring low latency, high availability, and cost efficiency.
- R&D Leadership: Conduct advanced research in NLP, reinforcement learning, and neural architecture search to drive product differentiation.
- Code Review & Mentorship: Establish best practices for code quality, documentation, and model governance; mentor junior engineers and data scientists.
- Collaboration: Partner with product teams to translate complex AI capabilities into user-friendly features.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related technical field, or equivalent practical experience.
- Experience: 5+ years of professional experience in machine learning engineering, with a strong focus on Python and deep learning frameworks.
- Technical Skills: Proficiency in PyTorch or TensorFlow; hands-on experience with Hugging Face Transformers and LLM fine-tuning.
- Infrastructure: Strong understanding of cloud-native architecture (AWS/Azure/GCP) and containerization technologies like Docker and Kubernetes.
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders.
- Problem Solving: Demonstrated history of solving difficult technical problems and optimizing existing systems for performance.