Job Description
We are Apex Future Tech, a pioneer in next-generation artificial intelligence, seeking a visionary Lead AI Engineer to define the technological landscape for the year 2026 and beyond. As we prepare for the next leap in generative intelligence, we need a technical leader who can bridge the gap between theoretical AI research and scalable production systems.
In this role, you won't just maintain current systems; you will architect the core infrastructure that powers the future of our enterprise solutions. You will work closely with top-tier researchers to implement cutting-edge Large Language Models (LLMs) and reinforcement learning frameworks, ensuring our products remain at the forefront of the industry.
Why Join Us?
- Work on projects that are shaping the definition of intelligence for the decade ahead.
- Competitive compensation package and equity opportunities.
- Flexible remote-first culture with a focus on innovation.
If you are ready to build the AI systems of tomorrow, today, apply now.
Responsibilities
- Architect and optimize high-performance AI models capable of handling next-generation data loads.
- Lead a team of ML engineers in deploying and maintaining scalable machine learning pipelines.
- Collaborate with product managers to translate complex AI capabilities into user-centric features.
- Ensure the ethical and responsible use of AI, implementing robust guardrails and compliance measures.
- Research and prototype novel algorithms to stay ahead of industry trends for 2026.
- Drive the migration of legacy systems to modern, cloud-native AI architectures.
Qualifications
- PhD or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
- Minimum of 5 years of experience in machine learning engineering and AI development.
- Deep expertise in Python, PyTorch, TensorFlow, or JAX.
- Proven track record of deploying large-scale ML models in production environments.
- Strong understanding of Natural Language Processing (NLP) and Large Language Model fine-tuning.
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker/Kubernetes).