Job Description
Are you ready to architect the intelligent systems of tomorrow? TechNova Solutions is seeking a visionary Senior AI Engineer to lead our R&D initiatives focused on the next generation of Generative AI and predictive modeling. As we look toward the technological landscape of 2026 and beyond, we need an expert who can bridge the gap between theoretical breakthroughs and scalable production environments.
Why Join Us?
At TechNova, we are not just building software; we are shaping the future of human-computer interaction. You will work in a dynamic environment where innovation is paramount, and your contributions will directly impact the roadmap for 2026.
Your Mission
In this role, you will spearhead the development of cutting-edge Large Language Models (LLMs), autonomous agents, and neural networks designed to solve complex, real-world problems.
Responsibilities
- Architect and deploy cutting-edge Large Language Models (LLMs) and generative AI frameworks tailored for 2026 use cases.
- Optimize existing AI models for low-latency, high-throughput production environments using advanced MLOps practices.
- Collaborate with cross-functional teams of data scientists, product managers, and engineers to integrate AI capabilities into core product offerings.
- Establish and enforce best practices for data privacy, ethics, and governance in AI development.
- Conduct research into emerging AI paradigms to keep TechNova ahead of the curve.
- Mentor junior engineers and provide technical leadership within the engineering department.
Qualifications
- Master’s degree or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
- 5+ years of professional experience in Machine Learning Engineering, with at least 2 years focusing on Generative AI or NLP.
- Proficiency in Python, PyTorch, and TensorFlow with deep understanding of deep learning architectures.
- Strong experience with vector databases (e.g., Pinecone, Milvus) and RAG (Retrieval-Augmented Generation) architectures.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Demonstrated ability to translate business requirements into technical AI solutions.