AI Engineer

- Equinix is seeking a highly experienced and hands-on engineering professional to drive the development, and delivery of next-generation AI-powered platforms and customer-facing agentic solutions. This role combines software engineering excellence with deep expertise in Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and modern cloud-native development practices
- The ideal candidate will lead the design and implementation of AI agents, establish engineering best practices, define quality and governance frameworks, and collaborate closely with product, architecture, security, and engineering teams to deliver scalable, secure, and reliable AI solutions that enhance customer experiences and business outcomes
- Design, build, deploy, and maintain LLM-powered agents and AI-driven applications
- Integrate AI capabilities into existing software platforms to enhance functionality, automation, and user experience
- Implement tool integration using Model Context Protocol (MCP) and agent-to-agent (A2A) communication patterns
- Design and implement automated evaluation frameworks, quality gates, benchmarking processes, and release criteria for customer-facing AI agents
- Establish guardrails, human-in-the-loop approval mechanisms, escalation workflows, and Responsible AI governance controls
- Monitor and optimize agent quality, accuracy, latency, reliability, adoption, observability, and operational costs
- Design, implement, and maintain Retrieval-Augmented Generation (RAG) architectures, vector databases, and knowledge engineering solutions
- Troubleshoot agent failures and continuously improve agent performance through prompt engineering, context optimization, workflow orchestration, and evaluation feedback loops
- Design, build, and manage automated CI/CD pipelines using GitHub Actions (GHA) and modern DevOps practices
- Collaborate with cross-functional teams to gather requirements, define technical solutions, and deliver exceptional customer experiences
- Troubleshoot and resolve issues related to client interfaces, APIs, integrations, and end-user interactions
- Provide hands-on leadership in software architecture, design, development, automation testing, deployment, and operational support
- Drive architecture decisions and establish engineering standards for AI-powered platforms and services
- Partner with product managers, architects, security teams, and engineering leaders to define technical strategy and execution plans
- Evaluate emerging AI technologies, frameworks, tools, and industry best practices to drive innovation and continuous improvement
- Provide technical estimates, identify risks, and contribute to roadmap planning, prioritization, and delivery execution
- Experience with Anthropic, OpenAI, Azure OpenAI, Amazon Bedrock, Vertex AI, or similar AI platforms
- Experience implementing automated testing, CI/CD pipelines, observability, and operational monitoring
- Experience in REST-based API development, API lifecycle management and/or client SDKs development
- 5+ years of programming with Python or Java languages
- Strong written and verbal communication skills, with the ability to explain complex concepts to non-technical stakeholders
- Bachelor’s degree in computer science, Software Engineering, Data Science
- 3+ years of experience in developing, deploying or optimizing ML models
- Experience with prompt engineering, context engineering, tool calling, retrieval systems, and multi-agent workflows
- 6+ years of experience in the full software development life cycle, including coding standards, code reviews, version control, build processes, and testing
- Strong customer-first mindset with a focus on platform usability and adoption
- Self-motivated, with a passion for learning and staying up-to-date with the latest technologies in the field
- Solid experience designing APIs, microservices, distributed systems, and event-driven architectures
- 6+ years of experience in software design, development, and algorithm related solutions
- A Master’s degree or PhD is highly desirable
- Ability to work independently and as part of a team, managing multiple tasks and projects simultaneously
- Ability to thrive in fast-paced, ambiguous environments
- 3+ years of hands-on experience building LLM-powered applications, AI assistants, or autonomous agent systems
- Monitor and analyze user feedback to drive continuous improvement in our applications
- Experience with agent frameworks and orchestration technologies such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar
- Experience implementing AI observability, tracing, evaluation platforms, and cost optimization solutions
- Knowledge-engineering experience including vector databases, embeddings, hybrid search, retrieval systems, and enterprise knowledge graphs
- Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform
- Experience working with MCP (Model Context Protocol), Agent-to-Agent (A2A) communication, and tool orchestration frameworks