Job Description
Join the Architects of Tomorrow.
Nexus Future Labs is at the forefront of the 2026 AI revolution. We are building the next generation of intelligent systems that will redefine human-machine interaction. As an AI/ML Engineer, you won't just write code; you will architect the neural foundations of our future products. If you are driven by complexity and obsessed with performance, this is your stage.
Why Nexus?
- Impact at Scale: Your models will power millions of daily transactions and decisions.
- Unlimited Autonomy: We trust our engineers to pick the best tools for the job.
- Top-Tier Compensation: Competitive base salary, equity, and performance bonuses.
The Role:
We are seeking a Senior AI/ML Engineer to lead the development of our proprietary Large Language Models (LLMs) and generative AI solutions. You will work closely with our research team to push the boundaries of what is possible in 2026 technology.
Responsibilities
- Architect & Deploy: Design, train, and deploy scalable machine learning models on cloud infrastructure (AWS/GCP).
- Model Optimization: Fine-tune pre-trained models (e.g., GPT, Llama) for specific niche applications to improve accuracy and reduce latency.
- R&D Leadership: Conduct cutting-edge research in Natural Language Processing (NLP) and computer vision to stay ahead of the 2026 technology curve.
- Collaboration: Partner with product managers and data scientists to translate business requirements into technical solutions.
- MLOps Implementation: Build robust CI/CD pipelines for machine learning models to ensure seamless production deployment.
- Ethical AI: Ensure all AI systems adhere to ethical guidelines and bias mitigation standards.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related field (or equivalent experience).
- Experience: 5+ years of professional experience in AI/ML, Deep Learning, or NLP.
- Programming: Expert proficiency in Python, PyTorch, or TensorFlow.
- Infrastructure: Deep understanding of cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Tools: Familiarity with MLOps tools (MLflow, Kubeflow) and version control (Git).
- Soft Skills: Exceptional problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.