Required Skills & Experience
Minimum of 7 years of experience in software engineering, AI/ML development, or a related technical field.
- Hands-on experience designing, building, and deploying AI/ML or intelligent automation solutions in production or near-production environments.
- Demonstrated experience with Microsoft Azure AI services, including Azure AI Foundry, Azure OpenAI, and Azure AI Search.
- Hands-on experience with Microsoft Copilot Studio and/or the Power Platform (Power Automate, Power Apps) for low-code AI solution development.
- Solid understanding of generative AI concepts, including retrieval-augmented generation (RAG), prompt engineering, vector databases, and orchestration patterns.
- Experience developing and integrating APIs and REST services, along with familiarity with enterprise integration platforms such as MuleSoft, Azure API Management, and Microsoft Graph connectors.
- Strong proficiency in Python, with demonstrated experience leveraging Generative AI libraries and modules (ex - LangChain, Semantic Kernel, OpenAI/Azure OpenAI SDKs), and vector database integrations to build and integrate LLM-powered solutions.
- Working knowledge of AI gateways, the Model Context Protocol (MCP), and emerging standards for agent interoperability and tool orchestration.
- Strong understanding of software development lifecycle (SDLC) methodologies, including Agile/Scrum practices.
- Experience with CI/CD pipelines, version control systems (e.g., Git), and modern DevOps practices.
- Understanding of AI security, data privacy, responsible AI principles, and regulatory compliance requirements.
- Understanding of AI FinOps concepts to be able to develop cost efficient AI solutions
- Strong analytical thinking and problem-solving abilities, with the capacity to navigate ambiguity and complex challenges.
- Excellent communication and collaboration skills, with the ability to effectively engage both technical and non-technical stakeholders.
Job Description
Develop and Deliver AI Assistants and Agent-Based Solutions o Design, build, test, and deploy intelligent digital assistants and AI agents using Microsoft Copilot Studio, Azure AI Foundry, Azure AI Search, and other Azure services. o Implement agentic workflows and multi-agent orchestration patterns using both low-code platforms (Copilot Studio, Power Platform) and pro-code approaches (Python, SDKs, APIs). o Develop and maintain retrieval-augmented generation (RAG) pipelines, including data ingestion, vector indexing, prompt engineering, and grounded responses leveraging enterprise data sources.
- Participate in the Full Software Development Lifecycle (SDLC) o Contribute to all phases of solution delivery, including requirements analysis, solution design, development, testing, deployment, and post-production support. o Write clean, well-documented, and maintainable code in accordance with engineering best practices and enterprise standards. o Support CI/CD pipelines and implement LLMOps practices for effective management of models, prompts, and agent lifecycles.
- Integrate AI Solutions with Enterprise Systems and Platforms o Develop and consume APIs, connectors, and integration workflows to connect AI solutions with enterprise data sources (e.g., SQL databases, file systems, SaaS platforms) and business applications. o Work with enterprise integration platforms (e.g., MuleSoft, Azure API Management, Azure Integration Services) and Microsoft Graph connectors to enable seamless and secure data access. o Leverage AI gateways, the Model Context Protocol (MCP), and emerging interoperability frameworks to enable secure, governed, and extensible communication between agents and enterprise systems.
- Contribute to Broader AI Initiatives o Collaborate with third-party partners to design and deploy video analytics and computer vision solutions (e.g., in-store customer experience, safety monitoring, and compliance use cases). o Support the development and operationalization of additional AI/ML use cases such as demand forecasting, personalization, and intelligent process automation.
- Support AI Governance, Security, and Responsible AI Practices o Implement responsible AI controls, including guardrails, content filtering, and security measures, in alignment with enterprise architecture and AI Center of Excellence (CoE) guidelines. o Ensure compliance with data privacy, security standards, and applicable regulatory requirements. o Contributes to the development and documentation of reusable AI patterns, frameworks, and reference architectures.
- Collaborate and Communicate Across Teams o Partner with solution architects, data engineers, product managers, and business stakeholders to translate requirements into high-impact AI solutions. o Provide technical input for estimation, planning, and delivery of project milestones. o Participate in architecture reviews and contribute to the continuous improvement of AI engineering standards and practices.
- Drive Continuous Learning and AI Innovation o Stay current with emerging AI tools, frameworks, models, and industry trends, particularly within the retail domain. o Promote knowledge sharing through technical demos, workshops, and documentation to strengthen AI capabilities across the organization.