Job Description
We are building the operating system for the autonomous future. As the Lead Agentic AI Architect, you will define the roadmap and technical strategy for our next-generation AI agents, specifically targeting the capabilities and paradigms expected to define the industry by 2026. You will bridge the gap between cutting-edge Generative AI research and scalable, production-grade software engineering.
Why Join Us?
We are a venture-backed startup at the forefront of the Agentic AI revolution. We are looking for a visionary leader to build systems that don't just generate text but can reason, plan, and execute complex multi-step tasks autonomously.
Responsibilities
- Architect Autonomous Workflows: Design and implement robust architectures for autonomous AI agents capable of multi-step reasoning, memory retention, and self-correction.
- 2026 Roadmap Strategy: Lead the technical vision for the next two years, integrating emerging multimodal models and self-improving algorithms.
- Model Optimization: Optimize inference pipelines for Large Language Models (LLMs) to ensure low-latency, high-throughput performance in edge environments.
- Multi-Agent Systems: Spearhead the development of 'Swarm Intelligence' architectures where multiple agents collaborate to solve complex business problems.
- Technical Leadership: Mentor a team of senior engineers and researchers, fostering a culture of innovation and excellence in AI engineering.
- R&D Integration: Evaluate and integrate new research from top-tier institutions to maintain a competitive edge in the rapidly evolving AI landscape.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field.
- Experience: 7+ years of experience in software engineering, with at least 3+ years specifically in AI/ML engineering or Applied NLP.
- Technical Stack: Deep proficiency in Python, PyTorch, or TensorFlow. Extensive experience with LLM frameworks (LangChain, LlamaIndex, Haystack).
- Agentic AI: Proven track record of building or deploying autonomous agents, RAG (Retrieval-Augmented Generation) systems, or AutoGPT-style applications.
- System Design: Strong understanding of distributed systems, microservices, and cloud infrastructure (AWS, GCP, or Azure).
- Communication: Exceptional ability to translate complex technical concepts into clear strategies for stakeholders and engineering teams.