AI DevOps Engineer (nearshore)

ConsultNet is recruiting an AI DevOps Engineer for a confidential client, a global designer and manufacturer of consumer and safety products. The role designs, develops, and deploys production-ready AI solutions, agentic workflows, and enterprise integrations to improve business processes, decision-making, and operational efficiency.

Responsibilities:

• Design, develop, and deploy AI-powered applications, agents, and intelligent automation solutions using commercial and open-source AI/LLM platforms
• Develop agentic AI workflows capable of reasoning, orchestrating tasks, interacting with systems, and supporting human decision-making
• Evaluate and leverage AI platforms and tools including AWS Bedrock, Microsoft Copilot, Copilot Studio, GitHub Copilot, Claude Code, and other emerging AI technologies
• Integrate AI capabilities with enterprise applications, APIs, databases, data platforms, and business workflows
• Develop AI integrations using technologies such as MCP (Model Context Protocol), APIs, connectors, tool calling, function calling, and other emerging AI integration patterns
• Partner with business and functional leaders to understand processes, identify high-value AI use cases, and translate business needs into technical solutions
• Build AI solutions that support automation, knowledge retrieval, reporting, analytics, decision support, exception management, root-cause analysis, and process optimization
• Develop and integrate AI agents with enterprise platforms such as ERP, MES, PLM, supply chain, finance, collaboration, and data systems
• Apply AI and LLM technologies to enterprise data to generate insights, automate analysis, and improve decision-making
• Design prompts, agent instructions, tools, workflows, and integrations that improve the reliability and effectiveness of AI solutions
• Evaluate AI models and platforms based on capability, cost, performance, security, scalability, and business value
• Monitor, test, measure, and continuously improve AI solutions for accuracy, reliability, performance, and measurable business impact
• Collaborate with data, software, automation, cybersecurity, and enterprise technology teams to deploy AI solutions into production environments
• Contribute to enterprise standards for AI governance, security, privacy, responsible AI, and data stewardship

Requirements:

• Hands-on experience developing agentic AI, generative AI, LLM, and/or intelligent automation solutions
• Strong understanding of AI agents, LLMs, prompt engineering, tool/function calling, RAG, and AI workflow orchestration
• Experience working with one or more enterprise AI/LLM platforms such as AWS Bedrock, Microsoft Copilot/Copilot Studio, Azure AI, OpenAI, Anthropic/Claude, or similar technologies
• Experience integrating AI solutions with enterprise systems, APIs, databases, data platforms, and business applications
• Familiarity with emerging AI integration technologies such as MCP, AI connectors, APIs, tool calling, or agent frameworks
• Experience using AI-assisted development tools such as GitHub Copilot, Claude Code, or similar AI coding/development platforms
• Ability to analyze complex business processes and translate business challenges into practical AI use cases and technical solutions
• Experience developing AI solutions for data analysis, reporting, knowledge retrieval, automation, or decision support
• Strong programming/software development experience with languages such as Python, JavaScript/TypeScript, or similar
• Strong analytical, problem-solving, communication, and cross-functional collaboration skills
• Experience in manufacturing, supply chain, industrial, consumer products, or other complex enterprise environments
• Experience with ERP, MES, PLM, supply chain, quality, production, or other enterprise business systems
• Experience with AWS, Azure, or other cloud platforms
• Experience with AI/ML frameworks, vector databases, RAG architectures, agent frameworks, or orchestration platforms
• Experience implementing MCP servers/clients or other AI-to-system integration patterns
• Background in process engineering, continuous improvement, digital transformation, software engineering, data engineering, or intelligent automation
• Experience establishing AI governance, security, compliance, evaluation, or responsible AI practices
• Experience working with multiple LLM providers and selecting models based on specific business and technical requirements