AI Solutions Lead IV

Responsibilities

The AI Solutions Architect (Lead) is responsible to Designs, implements, and oversees end-to-end enterprise AI architectures that translate business requirements into scalable, secure, and production-ready systems. Leads the technical design, development, and implementation of agentic AI systems to orchestrate user queries across heterogeneous data sources. Integrates data pipelines, models, LLMs, and enterprise applications while partnering closely with development, DevOps, and data engineering teams to deliver extensible AI architectures from prototype to production.

• Defines the overall system architecture, solution patterns, standards, and reference architectures for enterprise AI systems, selecting and governing AI/ML and platform technologies to ensure scalability, security, and production readiness.
• Leads platform and model integration across cloud services (Azure, AWS, or GCP), APIs, vector databases, orchestration frameworks, embeddings, vector search, memory, and reasoning workflows.
• Architects multistep, agentic workflows involving retrieval-augmented generation (RAG), tool-calling, multi-agent orchestration, and automated reasoning loops to orchestrate queries across heterogeneous data sources.
• Designs and deploys LLM-based systems including prompt strategy management, model evaluation, and system traceability and reliability mechanisms.
• Guides engineering and data teams through delivery from prototype to production, translating project requirements into extensible AI architectures and ensuring alignment across cross-functional teams.
• Evaluates emerging AI technologies to recommend optimal technology stacks, balancing innovation with enterprise stability and governance requirements.
• Ensures rigorous system scalability, security, and governance by applying responsible AI principles and enterprise security frameworks throughout the architecture lifecycle.
• Communicates architectural decisions, trade-offs, and technical strategies to executives and technical stakeholders.

Required Qualifications

• US Citizenship is requiredActive Top Secret (TS) clearance with eligibility for Sensitive Compartmented Information (SCI) and ability to obtain a Counterintelligence (CI) Polygraph.
• Bachelor's degree in Computer Science, AI/ML, Data Science, or related field along with 10 years of experience.
• Minimum of 8 years of experience designing, deploying, and operating AI systems, including agentic systems, with a proven track record delivering production-grade AI architectures.
• Minimum of 4 years of experience in AI and/or data engineering, including data pipelines, distributed systems, and microservice architectures.
• Deep expertise in Python, agent frameworks (such as LangChain, LlamaIndex, or equivalent), API design, backend engineering, vector databases, SQL engines, and cloud services.
• Demonstrated experience designing and deploying LLM-based systems, including RAG, tool-calling, and multi-agent patterns, with proven ability to evaluate models, manage prompt strategies, and design systems for traceability and reliability.
• Strong understanding of modern AI architectures including embeddings, vector search, orchestration layers, memory, and reasoning workflows.
• Working knowledge of DevOps tools and principles, including MLOps and platform engineering practices.
• Demonstrated leadership in cross-functional teams, guiding engineering and data teams through complex technical delivery.

Desired Qualifications

• Master's OR PhD degree
• Experience with Model Context Protocol (MCP) for standardized tool-calling and agent interoperability