Software Engineer 4 / Power Platform

Microsoft Power Platform (Power Apps Power Automate, PVA, Power BI)

• UI Path
• Langchain (or any other Agentic framework)
• Alteryx (Data transformation and automation)
• IDP (intelligent document processing)
• Microsoft Co-Pilot Studio
• Agentic AI (Hands on experience on Gitogenin/Vertex AI)

REQUIRED CERTIFICATIONS

• UI path Certified
• MS Power Platform Certified

In this role, you will:

• Lead moderately complex initiatives and deliverables within technical engineering environments
• Contribute to large scale planning of strategies across Consumer Technology
• Design, code, test, debug, and document applications and services including upgrades and deployments
• Review technical challenges that require in depth evaluation of technologies, procedures, and engineering approaches
• Resolve moderately complex issues while guiding teams to meet existing and emerging business needs
• Collaborate with peers, colleagues, and mid level managers to resolve technical challenges and meet project goals
• Lead projects and act as an escalation point, providing direction to less experienced engineers
• Design, develop, and deploy AI applications using enterprise APIs, LLMs, agent frameworks, and related technologies
• Implement prompt engineering, retrieval augmented generation, fine tuning, and agentic design patterns
• Integrate LLM models with existing enterprise systems and ensure that AI solutions meet governance, security, and compliance standards
• Troubleshoot complex application and model related issues and contribute to the continuous improvement of AI systems
• Assist and mentor engineers in advanced software development and AI engineering practices
• Stay informed of advancements in AI, LLMs, and agent frameworks and apply relevant updates to products and systems

• 4+ years of software engineering experience or equivalent through a combination of work experience, training, military service, or education
• 2+ years of experience working with Generative AI, large language models, or foundation models
• 2+ years of experience with either Google Cloud Platform, Azure, Kubernetes, or OpenShift
• 2+ years of experience with Python
• 2+ years of experience with REST API development and containerization technologies such as Docker and Kubernetes
• 2+ years of experience using Git for source code version control, including branching, pull requests, and collaborative development workflows, * Understanding of cloud security principles including identity and access management, encryption, and network security in public or hybrid cloud environments
• Experience working in highly regulated industries such as financial services
• Experience as a technical lead or architect, including mentoring senior engineers
• Experience integrating or contributing to open source AI or ML projects
• Experience integrating applications with enterprise data platforms, APIs, and secure data pipelines

• Strong communication and documentation skills to collaborate across engineering, product, and oversight teams Preferred Skills and Experience:

• Experience with Power Platform, including Power Apps and Dataverse
• Experience with UiPath or other enterprise automation tools
• Experience with LLM development using OpenAI, Anthropic, or Google Gemini models
• Experience with agentic frameworks and AI workflow orchestration
• Experience designing applications that incorporate retrieval augmented generation, fine tuning, and structured prompting
• Experience with vector databases and retrieval systems such as Elasticsearch, OpenSearch, Pinecone, or Weaviate
• Experience with LLM evaluation, observability, and monitoring including latency, cost, accuracy, grounding, drift detection, and safety assessments
• Familiarity with ML lifecycle tools and processes such as feature stores, model registries, and CI or CD pipelines for AI services
• Familiarity with responsible AI principles, compliance, and governance processes related to AI systems in regulated environments
• Experience optimizing AI application performance including prompt efficiency, model selection, caching, batching, and cost management.