AI Engineer, Product Software

Equinix•Redwood City, CA

20h•Onsite

About The Position

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.

Requirements

Bachelor’s degree in computer science, Software Engineering, Data Science
6+ years of experience in the full software development life cycle, including coding standards, code reviews, version control, build processes, and testing
6+ years of experience in software design, development, and algorithm related solutions
5+ years of programming with Python or Java languages
3+ years of experience in developing, deploying or optimizing ML models
3+ years of hands-on experience building LLM-powered applications, AI assistants, or autonomous agent systems
Experience with prompt engineering, context engineering, tool calling, retrieval systems, and multi-agent workflows
Solid experience designing APIs, microservices, distributed systems, and event-driven architectures
Experience with agent frameworks and orchestration technologies such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar
Experience working with MCP (Model Context Protocol), Agent-to-Agent (A2A) communication, and tool orchestration frameworks
Experience with Anthropic, OpenAI, Azure OpenAI, Amazon Bedrock, Vertex AI, or similar AI platforms
Knowledge-engineering experience including vector databases, embeddings, hybrid search, retrieval systems, and enterprise knowledge graphs
Experience implementing AI observability, tracing, evaluation platforms, and cost optimization solutions
Experience implementing automated testing, CI/CD pipelines, observability, and operational monitoring
Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
Experience in REST-based API development, API lifecycle management and/or client SDKs development
Strong written and verbal communication skills, with the ability to explain complex concepts to non-technical stakeholders
Self-motivated, with a passion for learning and staying up-to-date with the latest technologies in the field
Ability to work independently and as part of a team, managing multiple tasks and projects simultaneously
Monitor and analyze user feedback to drive continuous improvement in our applications
Strong customer-first mindset with a focus on platform usability and adoption
Ability to thrive in fast-paced, ambiguous environments

Nice To Haves

A Master's degree or PhD is highly desirable

Responsibilities

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