Artificial Intelligence and Machine Learning Engineer

Job Summary

The 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.

Responsibilities

• Build 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 portal

• Implement 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 context

• Build and maintain prompt engineering pipelines for developer-facing AI features: writing, versioning, evaluating, and iterating on prompts that power agentic assistants and explanation services

• Assist 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 layer

• Implement feedback loop collection for AI-powered features: capturing developer ratings, implicit signals, and usage patterns that feed model and prompt improvement over time

• Build evaluation pipelines to test AI feature quality — relevance, accuracy, and helpfulness — before and after changes to prompts, models, or ranking logic

• Integrate with LLM APIs such as Anthropic Claude, Amazon Bedrock, or OpenAI — handling authentication, rate limiting, error handling, and response parsing in production-grade code

• Work with the metrics engineer to instrument AI features with the telemetry needed to measure adoption, accuracy, and developer satisfaction

• Write clean, well-tested code and participate in code reviews, learning from senior engineers on the team

• Stay current on fast-moving developments in LLM tooling, agentic frameworks, and developer AI tooling and bring relevant ideas back to the team

Technical Requirements

• Proficiency in Python — the primary language for AI/ML feature development, prompt pipelines, evaluation frameworks, and agent component implementation

• Hands-on experience working with LLM APIs: Anthropic Claude, OpenAI, Amazon Bedrock, or equivalents — comfortable with prompt construction, API integration, response handling, and basic error management

• Familiarity with at least one agentic or LLM orchestration framework: LangChain, LangGraph, LlamaIndex, AutoGen, or equivalents — able to build simple agent pipelines and tool integrations

• Basic 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 is

• Familiarity with prompt engineering fundamentals: few-shot prompting, chain-of-thought, structured output prompting, and basic evaluation techniques

• Comfort 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 useful

• Basic 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 required

• Familiarity with Git and GitHub workflows; comfort working in a collaborative engineering environment with code reviews and version control

• Basic understanding of software engineering fundamentals: writing testable code, handling errors gracefully, and thinking about production reliability even for AI components

Qualifications

• 3+ 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 considered

• Demonstrated 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 built

• Genuine curiosity about how AI can improve developer workflows and reduce engineering toil — enthusiasm for the problem space matters as much as credentials at this level

• Ability to work collaboratively with senior engineers, receive and apply feedback well, and operate productively in an ambiguous, fast-moving environment

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; equivalent practical experience or bootcamp background with strong portfolio accepted

• Good written communication skills — able to document prompts, evaluation results, and design decisions clearly for the broader team

• Experience working in an Agile team environment with tools like Jira, Confluence, and GitHub