Summary
Our talented Data & AI Practice is made up of globally recognized experts - and there’s room for more analytical and ambitious data professionals. If you’re passionate about helping clients make better data-driven decisions to tackle their most complex business issues, let’s talk. Take your skills to a new level and launch a career where you can truly do what matters.
As an AI Engineer within our Data & AI Practice, you’ll leverage a client-centric approach to translate our client’s business strategy into solutions and services that lead to successful, impactful outcomes. You’ll engage in solutioning and support on the most strategic opportunities, intervene in high-risk complex deliveries, and contribute to shape the strategic direction around data architecture. Together we do what matters.
Key Responsibilities
- As an AI Engineer, you will collaborate with cross-functional teams to design, develop, and deploy AI solutions that address real-world business challenges.
- Design, build, test, deploy, and continuously improve enterprise AI agents using Microsoft Copilot Studio, including instructions, topics, knowledge sources, tools, generative orchestration, and analytics.
- Integrate Copilot Studio agents with Microsoft 365, Power Platform, Azure services, enterprise data, APIs, connectors, and Power Automate workflows to automate business processes.
- Apply responsible AI, security, data loss prevention, access control, environment strategy, monitoring, and application lifecycle management practices to Copilot Studio solutions.
- Work with cloud platforms (such as Azure, AWS, or GCP) to operationalize AI solutions, leveraging services like Copilot Studio, Power Platform and additionally Microsoft Foundry.
- Participate in code reviews, contribute to technical documentation, and support the integration of AI models into production systems.
- Communicate technical concepts and results to both technical and non-technical stakeholders.
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Skills and experiences
- 4–6 years of hands-on experience in AI engineering, including building and deploying models in production environments.
- Hands-on experience with Microsoft Copilot Studio, including building conversational or autonomous agents, grounding agents with enterprise knowledge, configuring topics and actions, testing, publishing, monitoring, and optimization.
- Experience integrating Copilot Studio with Power Automate, Dataverse, Microsoft 365, Azure AI services, REST APIs, standard or custom connectors, and Model Context Protocol is highly regarded.
- Experience with cloud AI/ML services (Azure ML, AWS SageMaker, GCP AI Platform) and containerization (Docker, Kubernetes) is highly regarded.
- Familiarity with MLOps practices, version control (Git), and CI/CD pipelines for model deployment is highly regarded.
- Working knowledge of data engineering concepts, including ETL processes and data pipeline orchestration.
- Strong problem-solving skills, attention to detail, and a passion for continuous learning.
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