Senior Lead Architect: Solution Architecture
On-siteJersey City, New Jersey, United States
Job Summary
Design and govern enterprise-scale architecture solutions for software applications and platform products, leveraging advanced AI, machine learning, and data engineering capabilities. Represent product families in technical governance bodies to propose enhancements to architecture governance and AI risk management practices. Provide strategic technical guidance to business stakeholders, engineering teams, contractors, and vendors while driving architectural decisions impacting product design and technical operations. Develop secure, high-quality production code for data-intensive and AI-driven applications, review and debug code to ensure best practices, and establish reuse-first, AI-enabled engineering patterns across the SDLC. Architect and govern agentic AI systems, including multi-agent workflows and human-in-the-loop controls, suitable for regulated financial services environments. Lead AI risk governance design, observability, and requirement explanation for production AI systems, shaping enterprise approaches to agent orchestration and state management at scale.
Required Qualifications
- Formal training or certification on architecture concepts
- 5+ years applied experience in AI/ML, cloud, and data engineering
- Minimum 12+ years of hands-on experience in system design, application development, testing, and operational stability
- Demonstrated expertise in designing and deploying production AI/ML systems, including LLM-based applications, embedding pipelines, vector stores, and agentic architectures with tool use, memory, and multi-step reasoning
- Experience evaluating model outputs for safety, accuracy, and latency in regulated environments
- Advanced proficiency in programming languages such as Java and Python
- Deep knowledge of software architecture, applications, and technical processes within disciplines such as cloud, artificial intelligence, machine learning, and data engineering
- Working knowledge of relational and NoSQL databases, data lake architectures, and large-scale data processing technologies (e.g., Spark/PySpark, Databricks, Snowflake)
- Experience with microservices, API design, Kafka, Redis, Memcached, observability tools (Dynatrace, Splunk, Grafana), and orchestration tools (Airflow, Temporal)
- Ability to evaluate and integrate AI-enabled capabilities into enterprise-grade architectures, meeting resiliency, security, and auditability requirements
- Practical cloud-native experience and ability to tackle complex design and functionality challenges independently
- Strong judgment and communication skills to influence technical direction across teams and stakeholders
Desired Qualifications
- Experience with modern data technologies such as Databricks or Snowflake
- Hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, CrewAI, or equivalent) and model serving infrastructure (Triton, AWS Bedrock, Azure Open AI)
- Familiarity with AI evaluation and observability—red-teaming, evals frameworks, prompt drift detection, and cost/latency monitoring for LLM workloads
- Understanding of agentic design patterns: React, plan-and-execute, reflection loops, and how to constrain agent autonomy in high-stakes financial workflows
- Awareness of the AI regulatory landscape in financial services, especially regarding AI use in decision-making
- Knowledge of the financial services industry and their IT systems
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