AI Application Engineer
Role details
Job location
Tech stack
Job description
- Develop leveraging Claude for real world applications.
- 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., The Senior Platform Engineering Lead is a pivotal senior-level engineering position responsible for driving the establishment, modernization, and implementation of robust applicati…
- 6 days ago
Requirements
5+ years experience, with 6 months or more experience working with Claude. Some Compliance experience within FS preferred, but even without is fine. Someone who can drive projects through based on passion and ability to develop quickly on Claude. We are seeking a highly skilled AI Development Engineer to design, build, and deploy advanced Claude AI-based .Net-stack based 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., * Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
- 3+ years of experience in AI/ML engineering, software development, or applied machine learning.
- Strong experience and passion of at least 6 months with Claude
- Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit learn, or similar.
- 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., * Some exposure to Compliance requirements within the Asset Management / 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.
