Role: AI Tech Lead
Location: Washington, DC 20433
Duration: 6 Months – Long Term
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.
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.
Cloud & DevOps Enablement
- 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.”
