Artificial Intelligence Engineer

• Position: AI Engineer
• Location: Remote
• Visa: NO H1 or OPT
• W2 only

AI Engineer Contractor Request

Seeking an experienced AI Engineer Contractor with expertise in the Azure ecosystem, specifically in machine learning and AI tools. The ideal contractor will have a strong background in both finance (asset management) and technology. The contractor will be responsible for developing and deploying AI solutions that enhance our investment strategies and operational efficiency. In addition, this role will involve coaching and mentoring existing team members to build their knowledge base and skill set in Azure-based AI technologies.

Key Responsibilities:
• Work closely with data science, engineering teams, and Front Office staff to design and implement AI solutions on Azure.
• Develop and deploy containerized solutions for AI deployments utilizing GitLab CI/CD pipelines.
• Advise on best practices for building scalable, secure, and efficient AI solutions.
• Initial focus will be delivering outcomes focused on retrieval-augmented generation (RAG).
• Fine-tune existing large language models (LLMs).
• Leverage financial data to develop predictive models, risk models, and other solutions that support investment decision-making.
• Assist in optimizing Azure infrastructure for performance and cost efficiency.
• Integrate AI models and tools into existing systems.
• Provide strategic guidance on AI and machine learning.
• Mentor and coach internal staff, helping them build proficiency with Azure AI tools and best practices.
• Lead training sessions and workshops to develop internal capabilities and foster a culture of continuous learning.

Qualifications:
• Minimum of 3 years of experience in AI engineering, with a focus on the Azure ecosystem.
• Previous experience in the asset management industry is required.
• Proficiency in Python and other relevant programming languages for AI development.
• Strong understanding of cloud architecture, data engineering, and containerization technologies (e.g., Docker, Kubernetes).
• Experience in the full machine learning lifecycle, from data collection to model deployment and monitoring.
• Solid knowledge of financial markets, asset management, and financial data (e.g., time series analysis, portfolio management, risk modeling).
• Excellent communication skills and the ability to explain complex technical concepts to non-technical stakeholders.
• Proven experience mentoring and coaching teams, with the ability to elevate the technical skills of others.