Job Description
Join Nexus Future Systems, a premier technology firm pioneering the next generation of artificial intelligence. We are seeking a visionary Senior Machine Learning Engineer to architect scalable, production-ready AI solutions that redefine human-computer interaction.
In this role, you will not merely implement existing models; you will push the boundaries of what is possible in Generative AI, Large Language Models (LLMs), and autonomous agents. You will work in a high-velocity, elite engineering environment where your code directly impacts millions of users.
If you are obsessed with data, possess a deep understanding of deep learning architectures, and want to solve the most complex engineering challenges of our time, we want to meet you.
Responsibilities
- Lead Model Development: Design and implement state-of-the-art Machine Learning and Deep Learning models, with a focus on Natural Language Processing (NLP) and Generative AI.
- Optimize Performance: Engineer high-performance inference pipelines, reducing latency and operational costs through model quantization, pruning, and caching strategies.
- System Architecture: Design robust MLOps infrastructure using Kubernetes, Docker, and cloud-native services to ensure model reliability and scalability.
- Data Strategy: Collaborate with data scientists and engineers to curate high-quality training datasets and implement Retrieval-Augmented Generation (RAG) architectures.
- Technical Leadership: Mentor junior engineers, conduct code reviews, and establish best practices for AI development within the organization.
- Research Integration: Stay ahead of the curve by integrating the latest research findings (e.g., Transformer architectures, Diffusion models) into our production stack.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, Statistics, or a related technical field.
- Experience: 5+ years of professional software engineering experience, with at least 3 years specifically focused on Machine Learning or Deep Learning.
- Programming: Expert proficiency in Python, PyTorch, TensorFlow, or JAX.
- Cloud & DevOps: Strong experience deploying models on AWS, GCP, or Azure using services like SageMaker, Vertex AI, or similar.
- AI Expertise: Deep understanding of Large Language Models (LLMs), prompt engineering, and fine-tuning techniques (PEFT, LoRA).
- Problem Solving: Demonstrated ability to troubleshoot complex system issues and optimize performance under tight constraints.