Job Description
Are you ready to architect the autonomous systems of tomorrow?
Nexus Horizon is at the forefront of the AI revolution, developing next-generation Agentic AI frameworks designed to redefine human-computer interaction. We are seeking a visionary Lead AI Engineer to join our elite R&D team and build the intelligent agents that will power the future.
In this pivotal role, you will bridge the gap between theoretical AI research and scalable production systems. You will lead the design of multi-agent workflows, optimize large language models (LLMs) for real-time decision-making, and ensure our AI solutions are robust, secure, and ethically aligned.
Why Join Nexus Horizon?
- Impactful Work: Build AI systems that solve complex global challenges.
- Future-Forward Tech: Work with the latest in LLMs, RAG, and Agentic AI (2026 standards).
- Competitive Compensation: Top-tier salary and equity package.
Responsibilities
- Architect and deploy scalable multi-agent systems using LLMs and autonomous reasoning frameworks.
- Design and implement Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and reduce hallucinations.
- Optimize model inference latency and cost for high-volume production environments.
- Collaborate with cross-functional teams to translate business requirements into cutting-edge AI solutions.
- Mentor junior engineers and data scientists, fostering a culture of innovation and technical excellence.
- Stay abreast of the latest research in AI safety, alignment, and generative modeling.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related field (PhD preferred).
- 5+ years of experience in software engineering, with at least 3 years specializing in AI/ML.
- Deep expertise in Python, PyTorch, and modern Machine Learning libraries (TensorFlow, JAX).
- Strong understanding of Large Language Models (GPT, Llama, Claude) and prompt engineering.
- Experience with orchestration tools such as LangChain, AutoGen, or LlamaIndex.
- Proven track record of deploying AI models to production (cloud infrastructure: AWS, GCP, or Azure).