Financial Advisory Senior AI Solution Architect
New
Nationwide, RemoteFull-TimeSenior
Salary165000 - 215000 USD per year
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Job Details
- Experience
- 8–12 years of professional experience
- Required Skills
- AWSPythonSQLGitMicrosoft AzureCI/CD
Requirements
- 8–12 years of professional experience in software engineering, AI/ML application development, or a closely related technical role with demonstrated production deployments
- Strong proficiency in Python, including experience with API development, data processing pipelines, and file generation libraries (e.g., python-pptx, openpyxl, or equivalent)
- Hands-on experience building applications that consume LLM APIs (OpenAI, Anthropic, Azure OpenAI, or similar), including prompt engineering, structured output parsing, and context window management
- Experience with document processing: PDF parsing, OCR, text extraction from structured and semi-structured documents
- Working knowledge of cloud infrastructure, preferably AWS (the practice’s current deployment environment) and/or Microsoft Azure. Experience deploying applications, databases (OpenSearch, SQL), and LLM services within enterprise cloud tenants.
- Familiarity with version control (Git), CI/CD practices, and production deployment workflows.
- Strong written and verbal communication skills.
- Demonstrated resilience and patience in navigating complex organizational environments.
- Experience in financial services, professional services, or consulting environments (preferred)
- Experience with Salesforce API integration and/or Salesforce development (Apex, SOQL, REST/SOAP APIs) (preferred)
- Experience building multi-agent or agentic AI systems (e.g., using LangChain, LangGraph, CrewAI, AutoGen, or custom orchestration frameworks) (preferred)
- Experience with RAG architectures, vector databases (Pinecone, Weaviate, Azure Cognitive Search), and embedding-based retrieval systems (preferred)
- Familiarity with Microsoft Power Platform (Power Automate, Power Apps, Power BI) for enterprise workflow automation and dashboarding (preferred)
- Experience with Seismic or similar enterprise content management platforms and their APIs (preferred)
- Background in data visualization, dashboard design, or business intelligence tooling (preferred)
- Experience with AWS services (EC2, Lambda, OpenSearch, Bedrock, S3) in addition to or in lieu of Azure experience (preferred)
Responsibilities
- Design, build, and deploy end-to-end AI applications that automate structured professional workflows, including document processing, content generation, data extraction, and programmatic file creation (PowerPoint, Excel).
- Develop and maintain Python-based orchestration pipelines that coordinate LLM API calls, document parsing, external system integrations, and formatted output generation.
- Build multi-agent architectures for complex tasks requiring specialized, parallelized processing.
- Implement retrieval-augmented generation (RAG) pipelines for context-aware content selection from internal knowledge repositories.
- Build and maintain API integrations with enterprise platforms including Seismic (content management), Salesforce (CRM), AWS, and Microsoft Azure services.
- Work within Huron’s IT security and data governance framework to ensure all solutions comply with information security policies.
- Deploy solutions using approved infrastructure (AWS, Azure, and internal compute environments) and establish CI/CD pipelines, version control, and monitoring for production applications.
- Embed directly with Financial Advisory professionals and subject matter experts to deeply understand their workflows, identify automation opportunities, and translate business requirements into technical specifications.
- Coordinate with Huron’s Digital–AI team on platform approvals, architectural standards, and shared infrastructure.
- Apply structured process analysis to identify high-effort, repetitive, or error-prone activities and redesign them using automation, agentic AI, and workflow orchestration.
- Provide delivery leadership across AI initiatives, including prioritization, sequencing of work, and coordination across technical and non-technical stakeholders.
- Monitor deployed solutions for quality, accuracy, and cost efficiency. Tune prompts, chunking strategies, and agent configurations based on production performance data.
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