AI Platform Engineer

• Design, develop, and deploy AI platform services including model lifecycle management, orchestration, and inference. 

• Build and enhance capabilities leveraging, Microsoft Copilot Studio,  AI Foundry, Agent Force, IBM Orchestrate, and Claude (Anthropic). 

• Implement and scale GenAI and LLM-based solutions across ETG use cases. 

• Establish and maintain MLOps and GenAIOps practices (CI/CD, monitoring, evaluation, governance). 

• Contribute to architecture decisions for cloud-native, distributed AI systems. 

• Participate directly in coding, design reviews, and troubleshooting of complex platform issues. 

• Foster a strong engineering culture focused on accountability, innovation, and collaboration. 

• Partner with product managers, data scientists, and business stakeholders to translate business needs into scalable platform solutions. 

• Ensure the AI platform supports a wide range of internal customers and use cases. 

• Define and execute the ETG AI Platform roadmap, balancing innovation with stability. 

• Communicate platform strategy, technical trade-offs, and progress to leadership. 

• Shape the long-term vision for ETG’s AI platform and ecosystem. 

• Evaluate emerging AI tools and frameworks for enterprise adoption. 

• Champion responsible AI practices including governance, security, and compliance. 

• Drive adoption of reusable AI capabilities and standardized patterns across teams. 

What we'll want you to have: (Job requirements and preferences)  

• 5+ years of experience in software engineering, application development, or related technical delivery roles. 

• Experience implementing production software in an Agile or product-oriented environment. 

• Demonstrated experience delivering AI-enabled, data-driven, automation, integration, or workflow solutions. 

• Hands-on experience with modern programming languages, APIs, cloud services, CI/CD practices, automated testing, and production support. 

• Working knowledge of AI technologies such as generative AI, ML services, prompt orchestration, retrieval patterns, agents, embeddings, or model integration. 

• Ability to understand implementation trade-offs and build pragmatic, supportable solutions within established architectural and enterprise guardrails. 

• Ability to communicate technical concepts clearly with business partners, analysts, product owners, engineers, and support teams. 

• Comfortable asking clarifying questions, challenging assumptions constructively, and translating business needs into actionable implementation details. 

• Skilled at demonstrating delivered functionality, incorporating feedback, and helping stakeholders understand how AI-enabled features affect business processes.