What are the responsibilities and job description for the AI Application Engineer position at Aptonet?
About the Role
We are seeking a highly skilled AI Development Engineer to design, build, and deploy advanced Claude AI-based, .NET-stack solutions that power next-generation products and internal platforms. This role is ideal for someone who thrives at the intersection of software engineering, AI application, and business outcomes. You will work closely with cross-functional teams to translate business needs into scalable, production-ready systems leveraging AI for coding.
The ideal candidate has at least six months of serious, hands-on experience building with Claude and the passion to drive projects independently from concept to production. Some exposure to compliance requirements within financial services is preferred but not required.
Key Responsibilities
- Develop Claude-based applications for real-world use cases on a .NET stack.
- Build scalable data pipelines and model-serving infrastructure for high-performance AI systems.
- Collaborate with product, engineering, and research teams to define technical requirements and deliver end-to-end AI solutions.
- Implement and maintain MLOps workflows, including model versioning, monitoring, and continuous improvement.
- Conduct experiments, evaluate model performance, and apply state-of-the-art techniques to improve accuracy and efficiency.
- Integrate AI models into production environments using APIs, microservices, or cloud-native architectures.
- Stay current with emerging AI technologies, frameworks, and best practices.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
- 5 years of experience in AI/ML engineering, software development, or applied machine learning.
- 6 months of demonstrated, hands-on experience building with Claude — this is a firm requirement.
- Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
- Solid understanding of data structures, algorithms, and distributed systems.
- Hands-on experience with model deployment, inference optimization, and API development.
- Familiarity with NLP, computer vision, generative AI, or LLM fine-tuning is a plus.
Preferred Qualifications
- Some exposure to compliance requirements within the asset management or hedge fund industry.
- Experience with vector databases, retrieval-augmented generation (RAG), or LLM orchestration frameworks.
- Knowledge of MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI.
- Background in reinforcement learning, generative modeling, or multimodal AI.
- Contributions to open-source AI/ML projects.