Senior AI / Software Engineer

As a Senior AI / Software Engineer at iGenius, your key responsibilities include building and maintaining Agentic AI systems, developing APIs for integrating AI models with financial platforms, ensuring compliance with industry standards, and optimizing model performance for real-time applications. You'll also work closely with cross-functional teams to align AI solutions with business objectives.

To qualify for the Senior AI / Software Engineer position at iGenius, candidates must hold a Bachelor’s or Master’s degree in Computer Science or Engineering, with at least 7 years of experience in AI engineering. A strong proficiency in Python programming, familiarity with data science libraries, and expertise in cloud infrastructure and distributed systems are crucial for success in this role.

The Senior AI / Software Engineer at iGenius plays a pivotal role in enhancing financial workflows by developing AI solutions that optimize portfolio construction and trading strategies. By embedding AI into financial systems, you help improve decision-making processes and operational efficiencies in regulated industries, making a significant impact on how financial data is utilized.

A successful Senior AI / Software Engineer at iGenius should be well-versed in Python, Docker, Kubernetes, cloud platforms like AWS and Azure, and have experience with machine learning frameworks such as TensorFlow or PyTorch. Familiarity with CI/CD tools and workflow management applications is also beneficial in this position.

The work culture at iGenius promotes innovation, collaboration, and continuous learning. As a Senior AI / Software Engineer, you'll be encouraged to take ownership of projects and actively contribute to a dynamic team environment that values creativity and problem-solving in the ever-evolving landscape of AI technology.

When answering this question, focus on specific projects you've worked on that demonstrate your ability to build and maintain scalable AI solutions, particularly in the financial sector. Highlight the technologies you used, the challenges faced, and how you overcame them to deliver impactful results.

Discuss your proficiency in Python and any relevant libraries such as NumPy, Pandas, and Scikit-learn. Mention your experience with deep learning frameworks like TensorFlow or PyTorch, and describe how you've applied these tools in previous projects to build effective AI models.

To tackle this question, explain your approach to model governance, security protocols, and compliance measures you've implemented in past projects. Emphasize the importance of auditing AI models and collaborating with relevant stakeholders to ensure adherence to regulatory standards.

Share a specific instance when you worked alongside data scientists and financial experts. Detail how you translated business requirements into technical solutions, demonstrating your effective communication and teamwork skills in driving project success.

Discuss your experience with model performance tuning techniques and the importance of monitoring metrics. Provide concrete examples of how you've implemented optimizations in real-time use cases to ensure models operate efficiently at scale.

Reflect on a specific challenge related to large language models in your past work. Talk about your problem-solving approach, the technologies you leveraged, and any innovative solutions you developed to achieve successful implementation.

Describe your knowledge of MLOps principles and any specific tools you've used, such as MLflow, Kubeflow, or similar platforms. Highlight instances where you've implemented MLOps processes to streamline AI model deployment and management.

When answering, outline your methodologies for data collection, cleaning, and validation. Stress the importance of data quality in training AI models and any frameworks or tools you’ve used to ensure data integrity throughout the project lifecycle.

Share relevant experiences where you've designed or worked with distributed systems within your AI projects. Explain the architecture used and how it contributed to the scalability and performance of AI solutions.

Express your dedication to staying updated in the fast-paced AI landscape. Mention specific resources, communities, or courses you engage with to enhance your skills, demonstrating your commitment to ongoing professional development.