Dear Candidates,
Greetings!
We have a contract role with one of our clients. Kindly find the below details.
Job Title: AI/GenAI Technical Architect with Pension Platforms Exp.
Location: Irvine, CA (3 days onsite/week)
Experience: 15–20+ years
Domain: Pension / Retirement Systems + AI/GenAI
Role Overview:
We are looking for a highly experienced Technical Architect to lead the design and delivery of next-generation pension and retirement platforms and AI-driven solutions. This role combines deep domain expertise in retirement systems with cutting-edge AI-first architecture, including GenAI, RAG systems, and domain-specific model development.
The ideal candidate will drive enterprise-scale transformation, define architectural strategy, and lead multi-disciplinary teams delivering scalable, high-performance, and intelligent platforms.
Key Responsibilities:
Architecture Leadership:
- Define and govern end-to-end architecture for large-scale pension and retirement platforms.
- Translate business requirements (BRD/FRD) into scalable, resilient solution architecture blueprints.
- Lead architecture reviews, establish standards, and ensure alignment with enterprise principles and non-functional requirements (performance, scalability, security).
AI/GenAI Architecture
- Design and implement GenAI-powered solutions, including:
- Retrieval-Augmented Generation (RAG) systems
- Domain-specific Small Language Models (SLMs)
- Agentic AI workflows and automation systems
- Lead model lifecycle activities including DAPT, SFT, LoRA fine-tuning, evaluation, and deployment.
- Architect seamless integration of AI capabilities into enterprise platforms.
Platform Engineering & Modernization
- Design microservices-based, API-first, event-driven architectures.
- Establish integration frameworks across legacy pension systems and modern platforms.
- Ensure high availability, fault tolerance, and low downtime for platforms managing large retirement portfolios.
Data & MLOps Strategy
- Define enterprise MLOps/LLMOps pipelines covering:
- Model training, validation, deployment, and monitoring
- Bias detection, drift management, and observability
- Enable intelligent data access platforms (e.g., AI-driven query systems and analytics).
Delivery & Execution
- Lead end-to-end execution from greenfield design through production deployment.
- Drive large transformation programs ensuring high data accuracy and minimal downtime.
- Oversee architecture consistency across multiple programs and workstreams.
Leadership & Stakeholder Management
- Lead and mentor large cross-functional distributed teams.
- Collaborate with business stakeholders and clients to align architecture with strategic outcomes.
- Drive governance including risk management, budget oversight, and executive reporting.
Required Skills & Experience
Core Technical Skills
- Strong expertise in:
- Microservices architecture & API-first design
- Event-driven systems and integration patterns
- Cloud platforms (AWS/GCP preferred)
- Deep understanding of enterprise architecture and large system design.
AI/ML & GenAI Expertise
- Hands-on experience with:
- RAG architectures and vector databases
- LLM/SLM fine-tuning techniques (DAPT, SFT, LoRA)
- Agent orchestration and AI workflows
- Knowledge of MLOps/LLMOps frameworks
- Experience optimizing models for efficient or on-device inference
Domain Expertise
- Strong experience in Pension / Retirement Systems
- Understanding of retirement lifecycle, policy administration, and large-scale data migration
- Prior exposure to financial services platforms is preferred
Leadership Experience
- Proven experience leading large engineering teams
- Experience managing architecture across multiple programs or accounts
- Strong stakeholder and client engagement skills
Preferred Qualifications
- AWS Certified Solutions Architect (or equivalent)
- Google Cloud / Generative AI certifications
- Experience building AI-enabled enterprise platforms
- Exposure to AI-driven automation and analytics solutions
Key Success Metrics
- Successful delivery of large-scale platform transformations
- Adoption and impact of AI-driven capabilities
- System performance, scalability, and uptime
- Data migration accuracy and deployment efficiency
Stakeholder satisfaction and business outcomes