Job Description
We are seeking a visionary Senior AI Engineer to join our elite R&D division. Nexus Systems is pioneering the next generation of generative intelligence, and we need a technical leader to architect scalable, high-performance Large Language Model (LLM) solutions. You will work at the intersection of deep learning, natural language processing, and cloud infrastructure to build products that redefine human-machine interaction.
In this role, you will own the end-to-end lifecycle of our AI models—from initial data curation and training to deployment and optimization. If you are passionate about the future of Artificial Intelligence and thrive in a fast-paced, high-impact environment, we want to hear from you.
Responsibilities
- Model Architecture & Training: Design and implement state-of-the-art transformer architectures and fine-tune pre-trained models (e.g., GPT-4, LLaMA, BERT) for specific enterprise use cases.
- RAG Pipelines: Build robust Retrieval-Augmented Generation systems to enhance model accuracy and reduce hallucinations.
- MLOps & Deployment: Develop and maintain CI/CD pipelines for machine learning models, ensuring scalable deployment on cloud platforms (AWS/Azure/GCP).
- Data Strategy: Curate, clean, and preprocess massive datasets, implementing advanced data engineering techniques to optimize model training efficiency.
- Performance Optimization: Conduct rigorous testing and optimization of model inference latency and throughput to meet real-time application requirements.
- Cross-Functional Leadership: Collaborate with product managers, data scientists, and engineers to translate business requirements into technical AI solutions.
Qualifications
- Education: MS or PhD in Computer Science, Machine Learning, Mathematics, or a related quantitative field.
- Experience: 5+ years of professional experience in AI/ML, with at least 2 years focusing on LLMs or NLP.
- Programming: Expert proficiency in Python, including frameworks such as PyTorch, TensorFlow, or JAX.
- Software Engineering: Strong software engineering principles with experience in distributed systems, microservices, and containerization (Docker/Kubernetes).
- Tools: Experience with vector databases (Pinecone, Weaviate, Milvus) and model orchestration tools (MLflow, LangChain).
- Communication: Exceptional ability to communicate complex technical concepts to non-technical stakeholders.