AI Tech Lead

Role: AI Tech Lead

Location: Washington, DC 20433

Duration: 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 .

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.

Enterprise Data & AI Integration

• 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.

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.

Technical Expertise

Artificial Intelligence

• Large Language Models (GPT, Claude, Gemini, Azure OpenAI, or equivalent).
• Agentic AI architectures.
• Multi-agent systems.
• Prompt Engineering and Context Engineering.
• Retrieval-Augmented Generation (RAG).
• Semantic Kernel, LangGraph, AutoGen, CrewAI, MCP, or equivalent frameworks.
• NLP, sentiment analysis, classification, summarization, and knowledge management.

Cloud & Platforms

• Microsoft Azure (preferred).
• AI Foundry, Azure OpenAI.
• AKS, Containers, Serverless Functions.
• Azure Storage, Cosmos DB, SQL Database.
• DevOps, CI/CD pipelines, Infrastructure as Code.

Software Development

• .NET/C#, Python, JavaScript/TypeScript.
• REST APIs and microservices.
• Enterprise integration patterns.
• Full SDLC experience.

Preferred Qualifications

• Experience designing Agentic AI solutions for Incident Management, Service Management, Grievance Management, Environmental & Social systems, or enterprise workflow automation.
• Experience with Responsible AI, AI Governance, and model monitoring.
• Microsoft Azure AI certifications and Scrum certifications.

Experience designing AI-enabled digital platforms for large international organizations, development institutions, or regulated industries.

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