Job Description
We are building the operating system for the year 2026. Nexus Future Labs is seeking a visionary Senior AI/ML Engineer to lead our Agentic AI initiatives. If you are passionate about pushing the boundaries of Large Language Models (LLMs) and building autonomous agents, we want to talk to you.
Why join us?
We are a Series B startup backed by top-tier VC firms, focused on democratizing AI for enterprise scalability. Our team is a mix of world-class researchers and engineering veterans from FAANG and OpenAI. You will have the autonomy to define the technical roadmap for our core product suite launching in 2026.
What you will do:
We are looking for someone who thrives in ambiguity and wants to build the future. Key areas include:
- Designing and training next-generation generative models tailored for specific verticals.
- Building robust MLOps pipelines to ensure model reliability and continuous learning.
- Collaborating with product and design to translate complex AI capabilities into intuitive user experiences.
- Optimizing model performance for edge deployment and high-throughput inference.
Requirements:
- 5+ years of professional experience in Machine Learning, Deep Learning, or Applied AI.
- Strong proficiency in Python, PyTorch, or TensorFlow.
- Experience with LLMs (HuggingFace, LangChain) and RAG architectures.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Excellent communication skills and the ability to mentor junior engineers.
Responsibilities
- Architect and deploy scalable generative AI models tailored for enterprise use cases.
- Design and implement MLOps pipelines to ensure model reliability and continuous learning.
- Collaborate with cross-functional teams to translate complex AI capabilities into intuitive user interfaces.
- Optimize inference pipelines for low-latency, high-throughput environments.
- Research and integrate cutting-edge advancements in NLP and multimodal AI.
- Establish best practices for code quality, testing, and documentation.
Qualifications
- PhD or MS in Computer Science, Statistics, Mathematics, or a related technical field.
- 5+ years of professional experience in Python and deep learning frameworks (PyTorch/TensorFlow).
- Proven track record of shipping production-level AI models.
- Deep understanding of LLM architectures, fine-tuning techniques, and RAG.
- Experience with cloud infrastructure (AWS/GCP/Azure) and containerization (Docker/K8s).
- Strong problem-solving skills and a passion for AI safety and ethics.