AI Tech Lead

Job Description

Role: AI Tech Lead

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Location: Washington, DC 20433

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Duration: Long Term

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

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Background

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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 .

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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.

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Objectives

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

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

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AI Strategy & Solution Architecture

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

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• Design and implement autonomous AI agents capable of planning, reasoning, orchestrating tasks, and interacting with enterprise systems.
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• Build intelligent workflows for incident management, document processing, knowledge management, customer support, and operational automation.
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• Develop prompt engineering, context engineering, memory management, and orchestration strategies to optimize AI performance.
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Enterprise Data & AI Integration

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• Integrate AI solutions with enterprise applications, knowledge repositories, APIs, and business workflows.
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• Design data architectures supporting vector databases, semantic search, enterprise knowledge graphs, and AI-ready data platforms.
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• Collaborate with data engineering teams to establish AI data pipelines and governance controls.
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Cloud & DevOps Enablement

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• Architect and deploy AI solutions on cloud platforms such as Microsoft Azure.
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• Implement CI/CD pipelines, infrastructure-as-code, monitoring, observability, and AI operations (AIOps) capabilities.
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• Optimize cloud resources for performance, scalability, and cost efficiency.
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Leadership & Capacity Building

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

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

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Artificial Intelligence

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

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

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

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• Experience designing Agentic AI solutions for Incident Management, Service Management, Grievance Management, Environmental & Social systems, or enterprise workflow automation.
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• Experience with Responsible AI, AI Governance, and model monitoring.
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• Microsoft Azure AI certifications and Scrum certifications.
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Experience designing AI-enabled digital platforms for large international organizations, development institutions, or regulated industries.

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