AI/ML Engineer

Who are we?Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet.  A place where bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future.Help us challenge assumptions, uncover bias, and remove barriers—because progress starts with fresh ideas. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.Job SummaryThe Software Engineer for AI/ML is a junior to mid-level engineering role within the Developer Experience Program (EDP), focused on building and integrating AI and machine learning capabilities that directly improve how developers work at Equinix. You will work on practical, high-impact AI features — intelligent work prioritization, agentic workflow components, LLM-powered developer assistants, and feedback loops that make the platform smarter over time. This is not a research role. You will be shipping AI features into production developer toolingIf you are early in your career but passionate about building real AI systems — not just running notebooks — and want to see your work directly reduce toil, cut PR cycle times, and help developers spend more time on meaningful work, this role is for you. You will work closely with the EDP engineering lead, product owner, and the broader platform engineering team.ResponsibilitiesBuild and integrate LLM-powered features into developer tooling: intelligent work recommendations, CI failure explanations, PR assist, and "what should I work on today?" surfaces in the developer portalImplement the v1 AI-assist layer on top of the contextual prioritization engine — taking a rules-based ranking system and extending it with signal-driven ML recommendations based on service ownership, operational risk, and developer contextBuild and maintain prompt engineering pipelines for developer-facing AI features: writing, versioning, evaluating, and iterating on prompts that power agentic assistants and explanation servicesAssist in the development of agent components within the Agentic Execution Framework — building discrete agent steps, tool integrations, and output parsers that plug into the broader orchestration layerImplement feedback loop collection for AI-powered features: capturing developer ratings, implicit signals, and usage patterns that feed model and prompt improvement over timeBuild evaluation pipelines to test AI feature quality — relevance, accuracy, and helpfulness — before and after changes to prompts, models, or ranking logicIntegrate with LLM APIs such as Anthropic Claude, Amazon Bedrock, or OpenAI — handling authentication, rate limiting, error handling, and response parsing in production-grade codeWork with the metrics engineer to instrument AI features with the telemetry needed to measure adoption, accuracy, and developer satisfactionWrite clean, well-tested code and participate in code reviews, learning from senior engineers on the teamStay current on fast-moving developments in LLM tooling, agentic frameworks, and developer AI tooling and bring relevant ideas back to the teamTechnical RequirementsProficiency in Python — the primary language for AI/ML feature development, prompt pipelines, evaluation frameworks, and agent component implementationHands-on experience working with LLM APIs: Anthropic Claude, OpenAI, Amazon Bedrock, or equivalents — comfortable with prompt construction, API integration, response handling, and basic error managementFamiliarity with at least one agentic or LLM orchestration framework: LangChain, LangGraph, LlamaIndex, AutoGen, or equivalents — able to build simple agent pipelines and tool integrationsBasic understanding of ML concepts relevant to ranking and recommendation: feature engineering, scoring functions, evaluation metrics such as precision, recall, and NDCG — a formal ML background is not required but comfort with these ideas isFamiliarity with prompt engineering fundamentals: few-shot prompting, chain-of-thought, structured output prompting, and basic evaluation techniquesComfort working with REST APIs and JSON — able to integrate with GitHub, Jira, and internal platform APIs to pull the context that AI features need to be usefulBasic familiarity with vector databases or semantic search concepts such as embeddings, similarity search, and retrieval-augmented generation — hands-on experience is a plus but not requiredFamiliarity with Git and GitHub workflows; comfort working in a collaborative engineering environment with code reviews and version controlBasic understanding of software engineering fundamentals: writing testable code, handling errors gracefully, and thinking about production reliability even for AI componentsQualifications3+ years of software engineering experience with meaningful exposure to AI/ML systems, LLM integrations, or data science in a production or near-production setting — internship or project experience consideredDemonstrated hands-on experience with LLM APIs or agentic frameworks through projects, coursework, open source contributions, or professional work — able to show examples of what you have builtGenuine curiosity about how AI can improve developer workflows and reduce engineering toil — enthusiasm for the problem space matters as much as credentials at this levelAbility to work collaboratively with senior engineers, receive and apply feedback well, and operate productively in an ambiguous, fast-moving environmentBachelor's degree in Computer Science, Engineering, Data Science, or a related field; equivalent practical experience or bootcamp background with strong portfolio acceptedGood written communication skills — able to document prompts, evaluation results, and design decisions clearly for the broader teamExperience working in an Agile team environment with tools like Jira, Confluence, and GitHubEquinix is committed to ensuring that our em