Agentic AI Architect

Mindlance; Washington (District of Columbia); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Mindlance, Inc. seeks an experienced Applied AI Engineering Lead to drive the design, development, and implementation of enterprise-scale Agentic AI solutions.

You will lead adoption of LLMs, multi-agent systems, intelligent automation, and cloud-native AI architectures to modernize processes and accelerate knowledge management. You will collaborate with product managers, enterprise architects, and engineers to translate strategy into scalable AI-powered platforms, establish governance, and

Qualifications

• Master's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, or related discipline.
• Minimum 10 years of professional experience in software engineering and enterprise solution delivery.
• Minimum 5 years of experience in cloud architecture and modern application development.
• Demonstrated experience delivering AI/ML and Generative AI solutions in large enterprises.
• Proven experience leading cross-functional engineering teams.
• Experience delivering solutions using Agile and Scrum methodologies.

Responsibilities

• Design enterprise AI architectures leveraging LLMs, Agentic AI frameworks, and multi-agent orchestration.
• Develop autonomous AI agents capable of planning, reasoning, and task orchestration.
• Build intelligent workflows for incident management, knowledge management, and support.
• Architect data platforms with vector databases and semantic search.
• Lead CI/CD, infrastructure-as-code, monitoring, observability, and AIOps.

Skills

Leadership experience

Team leadership

AI/ML delivery

Cross-functional

Agile/Scrum

Education

Master's degree in CS/IT/DS/AI/Engineering

Job description

Hybrid Onsite: 4 days per week from Day 1, with a full transition to 100% onsite anticipated soon

Background:

The organization is seeking an experienced Applied AI Engineering Lead to drive the design, development, and implementation of enterprise-scale Agentic AI solutions. The role will lead the adoption of Large Language Models (LLMs), multi-agent systems, intelligent automation, and cloud-native AI architectures to modernize business processes, accelerate knowledge management, enhance incident management capabilities, and improve operational efficiency.

The successful candidate will work closely with product managers, enterprise architects, business stakeholders, and engineering teams to transform strategic objectives into scalable AI-powered solutions.

Objectives

The primary objective of this assignment is to design and deliver enterprise-grade Agentic AI platforms and intelligent automation solutions that:

• Improve operational efficiency through AI-driven workflows.
• Enable intelligent knowledge discovery and content generation.
• Support incident detection, diagnosis, and remediation processes.
• Accelerate digital transformation initiatives across business functions.
• Establish AI engineering standards, governance, and best practices.

Scope of Work

AI Strategy & Solution Architecture

• Design enterprise AI architectures leveraging LLMs, Agentic AI frameworks, Retrieval-Augmented Generation (RAG), MCP Servers, Semantic Kernel, and multi-agent orchestration platforms.
• Define AI solution patterns, technical standards, and governance frameworks.
• Develop scalable, secure, and responsible AI architectures aligned with enterprise policies.

Agentic AI Solution Development

• Design and implement autonomous AI agents capable of planning, reasoning, orchestrating tasks, and interacting with enterprise systems.
• Build intelligent workflows for incident management, document processing, knowledge management, customer support, and operational automation.
• Develop prompt engineering, context engineering, memory management, and orchestration strategies to optimize AI performance.
• Integrate AI solutions with enterprise applications, knowledge repositories, APIs, and business workflows.
• Design data architectures supporting vector databases, semantic search, enterprise knowledge graphs, and AI-ready data platforms.
• Collaborate with data engineering teams to establish AI data pipelines and governance controls.
• Architect and deploy AI solutions on cloud platforms such as Microsoft Azure.
• Implement CI/CD pipelines, infrastructure-as-code, monitoring, observability, and AI operations (AIOps) capabilities.
• Optimize cloud resources for performance, scalability, and cost efficiency.

Leadership & Capacity Building

• Lead multidisciplinary engineering teams across onshore and offshore locations.
• Mentor engineers and architects in Agentic AI patterns, cloud-native engineering, and modern software development practices.
• Establish communities of practice and standards for AI engineering excellence.

Required Qualifications

• Master's degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering, or related discipline.
• Minimum 10 years of professional experience in software engineering and enterprise solution delivery.
• Minimum 5 years of experience in cloud architecture and modern application development.
• Demonstrated experience delivering AI/ML and Generative AI solutions in large enterprises.
• Proven experience leading cross-functional engineering teams.
• Experience delivering solutions using Agile and Scrum methodologies.

“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”