Job Description
The Future of Intelligence Starts Here.
We are Apex Logic Systems, a pioneer in next-generation computational architecture. As we prepare for the technological landscape of 2026, we are seeking a visionary Lead AI Engineer to spearhead our research and development division. You will be at the forefront of integrating generative models with quantum-ready infrastructure, pushing the boundaries of what is possible in artificial intelligence.
If you are obsessed with performance, scalability, and the future of tech, we want to hear from you.
Responsibilities
- Architect Future-Proof AI Systems: Design and implement scalable machine learning pipelines designed to operate efficiently in the 2026 computing environment.
- Lead Research & Development: Drive innovation in generative AI, natural language processing, and autonomous agent technologies.
- Optimize Performance: Reduce latency and increase throughput for high-volume data processing tasks using cutting-edge optimization techniques.
- Collaborate with Cross-Functional Teams: Partner with product managers, data scientists, and engineering leads to translate complex technical requirements into robust solutions.
- Mentor Engineering Talent: Foster a culture of continuous learning, guiding junior engineers and senior architects alike in best practices for AI deployment.
- Ensure Ethical AI: Implement fairness, accountability, and transparency (FAccT) principles in all model training and deployment stages.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: 5+ years of professional experience in machine learning engineering, with at least 2 years in a leadership or lead architect role.
- Core Tech Stack: Proficiency in Python, PyTorch, TensorFlow, and experience with distributed computing frameworks (e.g., Apache Spark, Kubernetes).
- Model Engineering: Deep understanding of large language models (LLMs), transformer architectures, and fine-tuning methodologies.
- Cloud & Infrastructure: Strong experience deploying models on cloud platforms (AWS, GCP, or Azure).
- Problem Solving: Proven track record of solving complex, ambiguous problems in high-pressure environments.