Job Description
Are you ready to architect the future of intelligence?
Nexus Future Systems is seeking a visionary Senior AI Architect (2026 Horizon) to lead the development of next-generation Generative AI and Autonomous Agent infrastructure. As we position ourselves at the forefront of the 2026 AI revolution, we need a technical leader who can bridge the gap between theoretical breakthroughs and scalable production systems.
In this pivotal role, you will define the architectural blueprint for our proprietary Large Language Model (LLM) ecosystem, ensuring ethical AI deployment, massive scalability, and real-time cognitive capabilities. Join a team of elite engineers and researchers dedicated to shaping the digital landscape of tomorrow.
Responsibilities
- Architect Scalable AI Infrastructure: Design and implement resilient, high-throughput pipelines for training and deploying large-scale Foundation Models and LLMs.
- Prompt Engineering & Orchestration: Develop advanced prompt engineering frameworks and agent orchestration systems to maximize model accuracy and efficiency.
- MLOps Strategy: Lead the implementation of CI/CD for machine learning, automating model retraining, fine-tuning, and evaluation loops.
- System Optimization: Optimize model inference latency and cost-efficiency using techniques like quantization, distillation, and edge deployment.
- Ethical AI Governance: Establish guidelines and guardrails to ensure AI outputs are unbiased, safe, and compliant with evolving regulations.
- Technical Leadership: Mentor a team of ML engineers and data scientists, conducting code reviews and architectural discussions.
Qualifications
- Education: Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
- Experience: 7+ years of experience in software engineering, with at least 4 years specializing in AI/ML architecture and production system design.
- Core Tech Stack: Deep proficiency in Python, PyTorch, and TensorFlow; extensive experience with LangChain, HuggingFace, and Vector Databases (Pinecone, Milvus).
- Cloud Expertise: Proven track record deploying scalable ML workloads on AWS or GCP using Kubernetes and serverless architectures.
- Model Engineering: Hands-on experience with fine-tuning LLMs (e.g., Llama, GPT-4, Claude) and optimizing inference performance.
- Problem Solving: Demonstrated ability to tackle complex, unstructured problems and deliver robust engineering solutions under tight deadlines.