Job Description
Are you ready to architect the intelligent systems of tomorrow?
Apex Innovations is on a mission to redefine the boundaries of human-computer interaction through advanced Generative AI. We are seeking a visionary Senior Machine Learning Engineer to lead our next-generation R&D division. You will be at the forefront of developing scalable Large Language Models (LLMs) and multimodal AI systems that power the enterprise solutions of 2026 and beyond.
In this role, you won't just be writing code; you will be shaping the ethical and technical framework for the future of AI. Join a team of world-class researchers and engineers committed to pushing the envelope of what's possible.
Responsibilities
- Lead Model Development: Design, train, and fine-tune state-of-the-art generative models, including LLMs and diffusion models, using proprietary and open-source architectures.
- System Architecture: Build robust, high-performance inference pipelines and scalable MLOps infrastructure to handle millions of requests per day.
- RAG Implementation: Develop advanced Retrieval-Augmented Generation systems to enhance model accuracy and reduce hallucinations in real-world applications.
- Performance Optimization: Conduct rigorous optimization of model latency and memory usage to ensure seamless user experiences on edge devices and cloud environments.
- Cross-Functional Collaboration: Partner with product managers, designers, and data scientists to translate complex research into deployable, user-centric features.
- Ethical AI Oversight: Establish and enforce best practices for AI safety, fairness, and transparency in model deployment.
Qualifications
- Education: Master’s or PhD in Computer Science, Machine Learning, or a related quantitative field from a top-tier institution.
- Core Tech Stack: Deep proficiency in Python, PyTorch, or TensorFlow with 5+ years of experience in research or production ML engineering.
- Generative AI Expertise: Proven experience with transformer architectures, Hugging Face ecosystem, and fine-tuning models like GPT, Llama, or Claude.
- MLOps Experience: Strong background in deploying models to production environments (AWS, GCP, or Azure) using Docker, Kubernetes, and MLflow.
- Mathematical Rigor: Solid understanding of linear algebra, calculus, probability, and statistics.
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems with innovative engineering solutions.