Job Description
Join Horizon AI Systems as we engineer the future of intelligent automation. We are not just predicting the trends of 2026; we are actively building the infrastructure that will define the next decade of human-computer interaction.
We are seeking a Generative AI & Agentic Workflow Engineer to lead the development of autonomous agents capable of complex reasoning, multi-step problem solving, and self-correcting behaviors. You will work at the intersection of LLMs (Large Language Models), vector databases, and orchestration frameworks to build systems that don't just respond to prompts but proactively drive business value.
Why join us?
- Impactful Work: Build systems that scale to millions of users.
- Future-Ready Tech: Work with cutting-edge tools before they become standard.
- Competitive Compensation: Top-tier salary and equity package.
Responsibilities
- Architect Agentic Systems: Design and implement complex multi-agent workflows that leverage LLMs for autonomous decision-making and task execution.
- Model Optimization: Fine-tune and optimize foundation models for specific vertical use cases, focusing on latency, cost-efficiency, and hallucination reduction.
- Integration & Deployment: Integrate AI capabilities into existing enterprise stacks using modern CI/CD pipelines and containerization (Docker/Kubernetes).
- R&D Leadership: Conduct research on emerging AI paradigms (e.g., reinforcement learning from human feedback, chain-of-thought prompting) to stay ahead of the 2026 technology curve.
- Mentorship: Guide a team of junior engineers and data scientists in best practices for AI development and ethical AI usage.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
- Experience: 5+ years of software engineering experience with at least 2 years specifically focused on Machine Learning or AI applications.
- Technical Skills: Proficiency in Python (PyTorch/TensorFlow), RESTful APIs, and cloud platforms (AWS/GCP/Azure).
- AI Expertise: Deep understanding of LLMs, RAG (Retrieval-Augmented Generation), and prompt engineering strategies.
- Problem Solving: Ability to tackle ambiguous problems and prototype solutions rapidly to validate hypotheses.
- Communication: Excellent technical writing and verbal communication skills for cross-functional collaboration.