Gen AI Engineer Lead

Summary

We are seeking an accomplished and strategic GenAI Product Engineering Lead to drive the design, development, and delivery of an enterprise-grade AI platform on Microsoft Azure. This role is ideal for a technical leader with deep expertise in Generative AI, agent-based systems, Power Platform, and cloud-native engineering. You will lead a multidisciplinary team to build scalable, secure, and intelligent solutions that transform business operations and deliver exceptional value to clients and internal users. Your leadership will be instrumental in shaping the technical direction, fostering innovation, and ensuring operational excellence across all aspects of platform engineering.

Position Summary

seeks an experienced and visionary GenAI Product Engineering Lead to architect, build, and scale our next-generation enterprise platform on Microsoft Azure. You will lead a multidisciplinary team of developers and engineers, driving innovation and operational excellence in building intelligent, secure, and scalable business solutions.

Key Responsibilities

Technical Leadership & Team Management

• Lead, mentor, and grow a high-performing engineering team, fostering a culture of innovation, accountability, and continuous improvement.
• Establish and enforce engineering best practices, focusing on code quality, reliability, and operational efficiency.
• Set technical direction and ensure alignment with enterprise goals and standards.

Enterprise Platform Architecture

• Architect scalable, secure, and resilient AI systems on Azure (and other cloud environments as needed), working with Large Language Models (LLMs), and collaborating with cross-functional teams to integrate AI into products and processes. A strong technical foundation in areas like Python, AI/ML frameworks, and cloud platforms, combined with project management and leadership skills, are essential for this role.
• Define Power Platform apps from end-to-end, including backend data pipelines.
• Define and enforce architectural standards, including microservices, event-driven design, and API-first principles.

Multi-Tenancy & SaaS Architecture

• Architect multi-tenant B2B environments with tenant-aware data partitions using Cosmos DB and Azure AD B2C/Entra ID to ensure data, security, and cost isolation.
• Build per-tenant vector stores, knowledge bases, and connectors to support customer-specific document ingestion and retrieval.
• Implement telemetry, metering, and consumption-based billing dashboards to track tenant-level usage and optimize resource allocation.

GenAI & Agent Integration

• Design and implement advanced GenAI solutions, including agent-based systems for automation, orchestration, and intelligent workflows.
• Integrate large language models (LLMs), Retrieval-Augmented Generation (RAG), and custom agents with business processes and user-facing applications.
• Lead design using frameworks such as Semantic Kernel, LangChain, AutoGen, or CrewAI to build orchestration among multiple specialized agents.
• Lead domain-specific fine-tuning and adaptation of large language models using Azure OpenAI Service or third-party frameworks (e.g., Tinker AI, LoRA, PEFT, or Delta Tuning).
• Design modular fine-tuning workflows to create tenant-aware or task-specific models that enhance personalization while maintaining shared governance and security.
• Implement automated retraining and evaluation loops to continuously improve response accuracy, tone, and compliance across tenants.
• Design agent toolkits that connect external APIs, databases, and Power Platform components for automated workflows.

Model Lifecycle and LLMOps

Oversee model evaluation, tuning, and continuous improvement cycles using reinforcement learning from human feedback (RLHF) or synthetic data augmentation.

Establish model-drift detection and retraining triggers to sustain model performance across customer domains.

Track usage analytics to measure model performance, accuracy, and user engagement.

Govern token usage, compute allocation, and cost optimization strategies using Azure Cost Management.

Manage versioning of models, prompts, and embeddings to ensure reproducibility, auditability, and traceability as part of the ongoing GenAI lifecycle operations.

Full Stack Management

• End-to-End Application Development: Design, develop, and maintain robust, scalable, and secure full-stack web applications.
• Frontend Development: Build dynamic and responsive user interfaces using React, TypeScript, and modern state management libraries.
• Backend & API Development: Architect and implement scalable backend services and RESTful APIs using Python (with frameworks like FastAPI or Flask) and caching technologies like Redis.
• Database Management: Design and manage data models and interact with both relational (SQL) and NoSQL databases (e.g., Cosmos DB).

Power Platform Enablement

• Collaborate with citizen developers and business stakeholders to enable rapid app development using Power Apps, Power Automate, and Power BI.
• Ensure seamless integration between custom Azure services and Power Platform components.

Data Engineering & Processing

• Lead the design and implementation of robust data ingestion, transformation, and processing pipelines using Azure Data Factory, Databricks, Synapse, or equivalent.
• Ensure data quality, governance, and compliance with enterprise and regulatory standards.
• Develop secure pipelines for fine-tuning data preparation, ensuring data labeling, anonymization, and quality scoring to meet Responsible AI standards.
• Leverage Azure ML pipelines or Databricks workflows to automate dataset curation and model retraining schedules.

Compute & Scalability

• Optimize compute resources using Azure Functions, AKS, and serverless architectures for cost-effective, high-performance workloads.
• Implement auto-scaling, load balancing, and failover strategies for mission-critical applications.

Security, Compliance & Reliability

• Champion security best practices, including identity management, data protection, and compliance (e.g., SOC2, GDPR).
• Establish monitoring, alerting, and incident response protocols for platform reliability and operational excellence.

Stakeholder Engagement & Communication

• Act as a technical liaison between engineering, product, business, and executive teams.
• Communicate complex technical concepts clearly to non-technical stakeholders.

Required Qualifications

• Proven experience leading engineering teams in building enterprise-grade platforms on Azure.
• Deep expertise in cloud architecture, distributed systems, and scalable frameworks.
• Advanced proficiency in GenAI, agent-based systems, and LLMs. Understanding of agent frameworks (e.g., LangChain, Semantic Kernel) and orchestration tools.
• Strong background in data engineering, including ETL, data lakes, and real-time processing.
• Demonstrated ability to design secure, compliant, and reliable cloud solutions.
• Generative AI Expert with: Direct experience integrating LLMs (e.g., Azure OpenAI), developing RAG workflows, and using vector databases (e.g., Azure AI Search).
• Strong proficiency in languages like Python, with experience in AI/ML frameworks such as TensorFlow, PyTorch, or Keras.
• Experience with LLMs, prompt engineering, agentic flows, and foundational AI techniques.
• Experience with cloud-native development (e.g., AWS, Azure) and building scalable microservices.
• Strong fundamentals in software engineering, system architecture, and lifecycle management
• Knowledge of enterprise security, compliance, and governance standards.
• Track record of delivering large-scale, multi-tenant platforms.
• Excellent leadership, communication, and stakeholder management skills.
• Experience working in Agile, Scrum, or Kanban environments.

Preferred Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
• Experience building and deploying fine-tuned or adapted LLMs using tools such as Tinker AI, Azure AI Studio, Hugging Face, or MosaicML.

Knowledge of parameter-efficient tuning methods (LoRA, PEFT, QLoRA) and prompt-based adaptation techniques.

Understanding of API Gateway & Identity federation (Azure API Management, Entra ID multi-tenant apps).

• Experience with Azure Data Factory, Synapse, Databricks, and Power Platform (Power Apps, Power Automate, Power BI).
• SaaS Experience: Experience building and supporting multi-tenant SaaS applications.
• Azure Ecosystem: Expertise with specific Azure services such as Azure AI Services, Azure App Services, Azure Functions, Azure DevOps, and Azure Kubernetes Service (AKS).
• Infrastructure as Code (IaC): Experience using IaC tools like Bicep or Terraform to manage cloud resources.
• Containerization: Proficiency with Docker and container orchestration tools like Kubernetes.
• Modern Frontend Tooling: Experience with TypeScript and state management libraries (e.g., Redux, Zustand).