Job Description
Are you ready to architect the next generation of intelligent systems? Nexus Future Systems is seeking a visionary Senior AI Engineer to lead our cutting-edge Generative AI initiatives. In this pivotal role, you won't just be building models; you will be defining the roadmap for the next decade of digital transformation.
We are looking for a thought leader who thrives in ambiguity and possesses the technical prowess to solve complex problems at scale. If you are passionate about Large Language Models (LLMs), multimodal AI, and building robust production systems, we want to hear from you.
Why join us?
We offer a competitive compensation package, comprehensive health benefits, and the opportunity to work on projects that will change the world. You will have the autonomy to experiment, innovate, and lead a high-performing team of engineers and data scientists.
Responsibilities
- Design and implement scalable Machine Learning pipelines and Generative AI architectures using Python, TensorFlow, and PyTorch.
- Lead the end-to-end lifecycle of AI projects, from data ingestion and processing to model training, evaluation, and deployment (MLOps).
- Collaborate with cross-functional teams of data scientists, software engineers, and product managers to translate business requirements into technical solutions.
- Optimize model performance for latency, throughput, and resource efficiency in cloud environments.
- Establish best practices for code quality, documentation, and ethical AI usage.
- Mentor junior engineers and conduct code reviews to foster technical growth within the team.
Qualifications
- Master’s degree in Computer Science, Artificial Intelligence, or a related field (or equivalent extensive experience).
- 5+ years of professional experience in software engineering and machine learning with a focus on NLP or Generative AI.
- Strong proficiency in Python, SQL, and cloud platforms (AWS, GCP, or Azure).
- Deep understanding of transformer architectures, fine-tuning techniques, and RAG (Retrieval-Augmented Generation) frameworks.
- Experience with MLOps tools (Docker, Kubernetes, MLflow, Airflow) and version control systems (Git).