Job Description
We are seeking a visionary Senior AI/ML Engineer to join our elite engineering team and spearhead our 2026 Roadmap. As a leader in the 2026 niche, you will define the architectural standards for next-generation Generative AI and Large Language Models (LLMs). You will work at the intersection of cutting-edge research and scalable production systems, ensuring our technology remains ahead of the curve.
In this role, you won't just maintain legacy systems; you will architect the future of intelligent automation. We are looking for a self-starter who thrives in ambiguity and is passionate about building robust, ethical, and high-performance AI solutions that solve real-world problems.
Why join us?
We offer a competitive package, equity options, and the opportunity to shape the technology landscape of the next decade.
Responsibilities
- Architect LLM Solutions: Design, train, and fine-tune large language models tailored for enterprise-level scalability and efficiency.
- 2026 Roadmap Execution: Lead the technical implementation of our 2026 strategic initiatives, focusing on Generative AI, Retrieval-Augmented Generation (RAG), and autonomous agents.
- System Optimization: Optimize inference pipelines and reduce latency for real-time AI applications running on cloud-native infrastructure.
- Model Governance: Establish best practices for model monitoring, bias detection, and data privacy compliance (GDPR/CCPA).
- Collaborative Innovation: Partner with product and research teams to translate complex business requirements into technical AI roadmaps.
- Mentorship: Mentor junior engineers and data scientists, fostering a culture of continuous learning and technical excellence.
Qualifications
- Education: Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field (PhD preferred).
- Experience: 5+ years of professional experience in Machine Learning, Deep Learning, or Natural Language Processing.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, or JAX.
- Cloud Expertise: Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).
- MLOps: Hands-on experience with MLOps tools, CI/CD pipelines, and model versioning.
- Problem Solving: Demonstrated ability to tackle complex algorithmic challenges and improve system performance metrics.