Job description
Join Apex Neural Systems, a leading AI research and deployment studio crafting scalable, real-world AI solutions. We’re looking for a Senior AI Engineer who thrives at the intersection of research and production to help shape the next generation of intelligent products.
In this role, you will own end-to-end ML pipelines, collaborate with researchers and engineers, and deliver robust, production-ready models that drive business impact.
What you’ll do includes designing and implementing models, building data pipelines, optimizing performance, and leading ML Ops practices in a fast-paced environment. You’ll collaborate with cross-functional teams to translate research into robust, scalable features for live products.
We offer a competitive salary, comprehensive benefits, and an environment that values curiosity, impact, and growth.
Responsibility
- Design, implement, and productionize scalable ML/AI models addressing real-world problems.
- Build end-to-end machine learning pipelines: data ingestion, preprocessing, training, evaluation, and deployment.
- Collaborate with data scientists to translate research into robust production features with measurable impact.
- Optimize models for latency, throughput, and cost; monitor performance in production and iterate.
- Lead code reviews, mentor junior engineers, and promote engineering best practices.
- Champion MLOps and CI/CD for ML, experiments tracking, and reproducible workflows.
- Deploy AI services on cloud platforms (AWS/GCP/Azure) using containers and orchestration (Docker, Kubernetes).
- Ensure data privacy, security, and compliance in all AI solutions.
Qualification
- BS/MS in Computer Science, Electrical Engineering, or a related field; or equivalent experience.
- 5+ years of hands-on AI/ML engineering experience with a proven track record in production deployments.
- Proficiency in Python; strong experience with TensorFlow and/or PyTorch.
- Experience building and maintaining end-to-end ML pipelines and MLOps practices.
- Solid knowledge of cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes).
- Strong understanding of data structures, algorithms, statistics, and mathematical foundations relevant to ML.
- Experience with NLP, Computer Vision, or other AI subfields is a plus.
- Excellent communication, collaboration, and problem-solving skills; ability to work across multidisciplinary teams.