AI/ML Architect (Finance)
Job Description
The AI/ML Architect will play a critical, hands-on leadership role within the AI Center of Excellence (CoE), shaping the architecture and strategic direction of enterprise-grade AI solutions. This position exists to design, build, and scale both traditional ML and Generative AI (GenAI) solutions that are robust, production-ready, and aligned with business objectives. The ideal candidate will have deep technical expertise and a strong execution mindset, someone who can drive solutions from idea to implementation.
This role works across a diverse ecosystem including AWS, Salesforce, Snowflake, and Oracle to support prioritized enterprise initiatives.
ACCOUNTABILITIES: (The primary functions, scope and responsibilities of the role)
Engineering and Architecture:
- Lead the hands-on architecture, development, and deployment of production-grade AI/ML systems, ensuring scalability, reliability, performance, and cost-efficiency.
- Architect traditional ML solutions (e.g., classification, regression, recommendation systems) and advanced GenAI systems including Retrieval-Augmented Generation (RAG) and Agentic AI.
- Design and implement cloud-native AI/ML pipelines using cloud platforms.
- Evaluate, prototype, and build PoCs regularly to test architectural decisions, validate feasibility, and accelerate solution delivery.
- Integrate and deploy multiple LLMs (e.g., from OpenAI, Claude, Gemini, LLaMA, Hugging Face) and vector databases (e.g., Pinecone, Qdrant, pgvector, Milvus, Weaviate).
- Create reusable frameworks and solution templates that drive consistency, speed, and quality across AI initiatives.
- Ensure all solutions are aligned with responsible AI standards, security best practices, and enterprise governance.
Design, Innovation & Strategy:
- Explore and implement emerging AI paradigms, including agentic AI, Model Context Protocol (MCP), and Google's Agent-to-Agent (A2A) protocol.
- Drive innovation by recommending and evaluating GenAI tools, third-party orchestration frameworks, and SaaS integrations.
- Develop a forward-looking technical roadmap that balances short-term deliverables with long-term innovation.
- Stay current with cutting-edge trends in LLMOps, AI observability, and intelligent automation platforms.
- Leverage AI coding assistants (e.g., Claude Code, AWS Bedrock, GitHub Copilot) to boost productivity.
Collaboration and Enablement:
- Collaborate with data scientists, ML engineers, software engineers, and enterprise architects to translate business needs into scalable AI solutions.
- Provide technical guidance, mentorship, and architectural direction to teams working across the AI/ML lifecycle.
- Work in agile teams, contributing hands-on while also shaping backlog priorities and solution design.
- Partner cross-functionally to integrate AI into enterprise systems (e.g., Salesforce, Snowflake, Oracle).
REQUIRED QUALIFICATIONS: (Minimum qualifications needed for this position including education, experience, certification, knowledge and/or physical requirements)
Knowledge of:
- Scalable architecture patterns for traditional ML and GenAI.
- Multi-cloud AI/ML services including AWS (SageMaker, Bedrock etc) and at least one of Azure (ML, OpenAI) or GCP (Vertex AI).
- Strong familiarity with multiple LLMs and embedding models (e.g., OpenAI, Anthropic, Meta, Google, Hugging Face).
- Proficiency in contextual memory and multiple vector databases for semantic search.
- MLOps and LLMOps practices, including CI/CD, model monitoring, versioning, drift detection, and governance.
- Prompt engineering and management practices, including prompt versioning, A/B testing of prompts, and experience with prompt management tools
- AI/ML observability stacks such as Weights & Biases, Langsmith or similar tools.
Required Skills and Abilities:
- Hands-on experience designing and building AI/ML solutions from prototype to production.
- Strong Python development skills, including frameworks and libraries for ML, GenAI, and software engineering best practices.
- Experience with TensorFlow and/or PyTorch for training and deploying models.
- Deep understanding of software engineering, including modular design, testing, version control (Git), and CI/CD pipelines.
- Proven track record of building and running PoCs to validate architecture and feasibility.
- Experience working in agile environments, participating in sprints and cross-functional delivery.
- Ability to communicate technical concepts clearly to a wide range of stakeholders.
- Eagerness and ability to quickly learn and apply new AI/ML and automation technologies.
Education and/or Experience:
- Bachelor's degree in computer science, Engineering, or a related field (Master's preferred).
- 8+ years of experience in AI/ML engineering or architecture roles.
- Strong portfolio of real-world deployments in both traditional ML and GenAI.
PREFERRED QUALIFICATIONS: (Additional qualifications that may make a person even more effective in the role, but are not required for consideration)
- Master's or PhD in a technical field.
- Experience architecting agentic AI systems and multi-agent orchestration workflows.
- Experience in regulated industries, especially healthcare or finance.
- AI/ML certifications from AWS, Azure, or GCP.
- Contributions to open-source AI/ML projects or published research.
The AI/ML Architect will play a critical, hands-on leadership role within the AI Center of Excellence (CoE), shaping the architecture and strategic direction of enterprise-grade AI solutions. This position exists to design, build, and scale both traditional ML and Generative AI (GenAI) solutions that are robust, production-ready, and aligned with business objectives. The ideal candidate will have deep technical expertise and a strong execution mindset, someone who can drive solutions from idea to implementation.
This role works across a diverse ecosystem including AWS, Salesforce, Snowflake, and Oracle to support prioritized enterprise initiatives.
ACCOUNTABILITIES: (The primary functions, scope and responsibilities of the role)
Engineering and Architecture:
- Lead the hands-on architecture, development, and deployment of production-grade AI/ML systems, ensuring scalability, reliability, performance, and cost-efficiency.
- Architect traditional ML solutions (e.g., classification, regression, recommendation systems) and advanced GenAI systems including Retrieval-Augmented Generation (RAG) and Agentic AI.
- Design and implement cloud-native AI/ML pipelines using cloud platforms.
- Evaluate, prototype, and build PoCs regularly to test architectural decisions, validate feasibility, and accelerate solution delivery.
- Integrate and deploy multiple LLMs (e.g., from OpenAI, Claude, Gemini, LLaMA, Hugging Face) and vector databases (e.g., Pinecone, Qdrant, pgvector, Milvus, Weaviate).
- Create reusable frameworks and solution templates that drive consistency, speed, and quality across AI initiatives.
- Ensure all solutions are aligned with responsible AI standards, security best practices, and enterprise governance.
Design, Innovation & Strategy:
- Explore and implement emerging AI paradigms, including agentic AI, Model Context Protocol (MCP), and Google's Agent-to-Agent (A2A) protocol.
- Drive innovation by recommending and evaluating GenAI tools, third-party orchestration frameworks, and SaaS integrations.
- Develop a forward-looking technical roadmap that balances short-term deliverables with long-term innovation.
- Stay current with cutting-edge trends in LLMOps, AI observability, and intelligent automation platforms.
- Leverage AI coding assistants (e.g., Claude Code, AWS Bedrock, GitHub Copilot) to boost productivity.
Collaboration and Enablement:
- Collaborate with data scientists, ML engineers, software engineers, and enterprise architects to translate business needs into scalable AI solutions.
- Provide technical guidance, mentorship, and architectural direction to teams working across the AI/ML lifecycle.
- Work in agile teams, contributing hands-on while also shaping backlog priorities and solution design.
- Partner cross-functionally to integrate AI into enterprise systems (e.g., Salesforce, Snowflake, Oracle).
REQUIRED QUALIFICATIONS: (Minimum qualifications needed for this position including education, experience, certification, knowledge and/or physical requirements)
Knowledge of:
- Scalable architecture patterns for traditional ML and GenAI.
- Multi-cloud AI/ML services including AWS (SageMaker, Bedrock etc) and at least one of Azure (ML, OpenAI) or GCP (Vertex AI).
- Strong familiarity with multiple LLMs and embedding models (e.g., OpenAI, Anthropic, Meta, Google, Hugging Face).
- Proficiency in contextual memory and multiple vector databases for semantic search.
- MLOps and LLMOps practices, including CI/CD, model monitoring, versioning, drift detection, and governance.
- Prompt engineering and management practices, including prompt versioning, A/B testing of prompts, and experience with prompt management tools
- AI/ML observability stacks such as Weights & Biases, Langsmith or similar tools.
Required Skills and Abilities:
- Hands-on experience designing and building AI/ML solutions from prototype to production.
- Strong Python development skills, including frameworks and libraries for ML, GenAI, and software engineering best practices.
- Experience with TensorFlow and/or PyTorch for training and deploying models.
- Deep understanding of software engineering, including modular design, testing, version control (Git), and CI/CD pipelines.
- Proven track record of building and running PoCs to validate architecture and feasibility.
- Experience working in agile environments, participating in sprints and cross-functional delivery.
- Ability to communicate technical concepts clearly to a wide range of stakeholders.
- Eagerness and ability to quickly learn and apply new AI/ML and automation technologies.
Education and/or Experience:
- Bachelor's degree in computer science, Engineering, or a related field (Master's preferred).
- 8+ years of experience in AI/ML engineering or architecture roles.
- Strong portfolio of real-world deployments in both traditional ML and GenAI.
PREFERRED QUALIFICATIONS: (Additional qualifications that may make a person even more effective in the role, but are not required for consideration)
- Master's or PhD in a technical field.
- Experience architecting agentic AI systems and multi-agent orchestration workflows.
- Experience in regulated industries, especially healthcare or finance.
- AI/ML certifications from AWS, Azure, or GCP.
- Contributions to open-source AI/ML projects or published research.
