Job Description
We are pioneering the next generation of intelligent systems designed for the technological landscape of 2026 and beyond. Nexus Future Labs is seeking a visionary Senior AI Engineer to lead the development of scalable, agentic AI architectures. In this pivotal role, you will bridge the gap between theoretical machine learning advancements and production-grade applications, ensuring our solutions remain at the forefront of the industry.
You will work in a high-performance environment where innovation is not just encouraged but expected. You will collaborate with a diverse team of data scientists, software engineers, and product strategists to build the foundational models that will define the future of automation.
Why join us?
- Work on cutting-edge Agentic AI and Autonomous Systems.
- Competitive compensation package and equity options.
- Flexible remote-first hybrid work model.
- Access to state-of-the-art compute infrastructure and research grants.
Responsibilities
- Design and implement robust, scalable machine learning pipelines and data architectures.
- Lead the research and deployment of Large Language Models (LLMs) and multimodal AI agents.
- Optimize model inference for edge devices and cloud environments to ensure low latency.
- Establish best practices for MLOps, model monitoring, and ethical AI governance.
- Collaborate with cross-functional teams to translate complex business requirements into technical solutions.
- Mentor junior engineers and contribute to the technical vision of the engineering department.
Qualifications
- Master’s or PhD in Computer Science, Mathematics, or a related field (or equivalent practical experience).
- 5+ years of professional experience in machine learning, AI, or data science.
- Deep expertise in Python, PyTorch, TensorFlow, or similar frameworks.
- Proven track record of deploying production-grade models handling >1M requests/day.
- Experience with vector databases (Pinecone, Weaviate, Milvus) and RAG architectures.
- Strong understanding of distributed systems, cloud platforms (AWS/GCP), and containerization (Docker/Kubernetes).