What are the responsibilities and job description for the Investment Management AI Architect - Direct Client position at Redef Corp?
Overview
We are seeking a hands-on AI Architect to design and implement AI-driven solutions across the organization. This role will focus on building intelligent AI agents, integrating LLM and RAG (Retrieval-Augmented Generation) technologies, and establishing scalable AI architecture on Azure to enhance business efficiency and drive data-driven decision-making, including generating investment insights.
Key Responsibilities
- Define and lead enterprise AI architecture, strategy, and roadmap across the organization.
- Design, build, and deploy AI agents, agentic frameworks, and LLM-based solutions.
- Implement RAG-based systems, including knowledge retrieval, vector search, and domain-specific AI models.
- Integrate Generative AI into enterprise applications, workflows, and data platforms.
- Collaborate with business, data, and engineering teams to identify and prioritize AI use cases.
- Re-architect legacy systems to incorporate AI-driven capabilities.
- Establish scalable AI infrastructure, governance, and best practices on Azure.
- Develop solutions leveraging knowledge graphs and domain-specific data models.
- Work with structured and unstructured financial data to generate insights and improve decision-making.
- Define KPIs, drive adoption, and lead cross-functional teams to deliver AI initiatives at scale.
Required Skills
- 10 years of experience in software engineering, architecture, or enterprise systems.
- 3 years of hands-on experience with AI/ML, Generative AI, or LLM-based solutions.
- Strong programming skills in Python with frameworks such as PyTorch or TensorFlow.
- Experience with LLMs, RAG, AI agents, LangChain, OpenAI APIs, or similar technologies.
- Experience building end-to-end AI solutions (from concept to production).
- Knowledge of APIs, microservices, data pipelines, and cloud platforms (Azure preferred).
- Experience with knowledge graphs, vector databases, and semantic search.
- Familiarity with MLOps, model deployment, and real-time inference optimization.
- Strong experience working with cross-functional teams and leading large-scale initiatives.
- Experience in financial services or the wealth/asset management domain is preferred.
Nice to Have / Preferred
- Experience building domain-specific LLMs or post-training models.
- Exposure to real-time AI systems and performance optimization.
- Prior experience driving measurable business impact (e.g., efficiency gains, revenue growth, automation).