Job Description
We are seeking a visionary Senior Generative AI Engineer to join our elite team and architect the technological roadmap for the year 2026. At Nexus Future Labs, we are building the infrastructure that will define the next era of human-computer interaction. You will be at the forefront of integrating Large Language Models (LLMs) and multimodal agents into scalable production environments.
As we approach 2026, the demand for intelligent, autonomous systems is skyrocketing. We need a technical leader who not only understands the current state of the art but can anticipate the breakthroughs of tomorrow. You will work with a cross-functional team of researchers, product managers, and designers to build ethical, high-performance AI systems.
Why Join Us?
- Work on cutting-edge GenAI research applied to real-world problems.
- Competitive equity and compensation packages aligned with 2026 market standards.
- Flexible remote-first culture with a focus on innovation.
Responsibilities
- Design, train, and fine-tune large-scale generative models (e.g., GPT-4 architecture, LLaMA variants) tailored for enterprise applications.
- Optimize model inference pipelines to ensure sub-millisecond latency and high throughput at scale.
- Implement robust evaluation frameworks and guardrails to ensure AI safety, fairness, and compliance with emerging regulations.
- Collaborate with data scientists to curate high-quality synthetic training datasets for the 2026 roadmap.
- Mentor junior engineers and researchers, fostering a culture of technical excellence and continuous learning.
- Lead technical architecture reviews to ensure system scalability and maintainability.
Qualifications
- Master’s or PhD in Computer Science, Artificial Intelligence, or a related quantitative field (or equivalent professional experience).
- 5+ years of experience in machine learning engineering, with at least 2 years specifically in generative AI or NLP.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Strong understanding of distributed systems, cloud infrastructure (AWS/GCP), and containerization (Docker/Kubernetes).
- Experience with MLOps tools such as MLflow, Kubeflow, or Vertex AI.
- Proven track record of deploying production-ready AI models.