Job Description
We are building the infrastructure for tomorrow, and we are looking for a visionary Senior AI/Agentic Engineer to define the technical roadmap for the 2026 era. In this pivotal role, you will architect the next generation of autonomous AI agents capable of complex reasoning, multi-step execution, and self-improvement.
At Nexus Future Tech, we don't just predict the future; we build it. You will be at the forefront of integrating Large Language Models (LLMs) with autonomous agents, creating systems that redefine human-machine interaction. If you are passionate about pushing the boundaries of Generative AI and want to shape the standard for 2026, we want to hear from you.
Why Join Us?
- Future-Proof Role: Directly influence the 2026 product roadmap.
- Top-Tier Compensation: Competitive salary and equity package.
- State-of-the-Art Stack: Work with the latest in PyTorch, LangChain, and proprietary models.
Responsibilities
- Design and implement scalable autonomous AI agents using LangChain and AutoGen frameworks.
- Architect multi-modal systems capable of processing text, vision, and audio inputs simultaneously.
- Optimize LLM inference latency and cost-effectiveness for high-volume production environments.
- Research and prototype novel architectures for 2026 scalability, including Retrieval-Augmented Generation (RAG) advancements.
- Collaborate with product managers and data scientists to translate complex requirements into robust technical solutions.
- Ensure ethical AI practices and bias mitigation in automated decision-making processes.
Qualifications
- Master’s degree in Computer Science, Artificial Intelligence, or a related field (PhD preferred).
- 5+ years of professional experience in software engineering with a focus on AI/ML.
- Deep expertise in Python, PyTorch, TensorFlow, and modern JavaScript/TypeScript.
- Proven experience building and deploying multi-agent systems and autonomous workflows.
- Strong understanding of transformer architectures, fine-tuning techniques (LoRA, QLoRA), and vector databases.
- Experience with cloud infrastructure (AWS/GCP) and containerization (Docker/Kubernetes).