AI Engineer

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.

About Equinix: 
 

Equinix is the world’s digital infrastructure company, operating 280 data centers across the globe and providing interconnections to all the key clouds and networks. Businesses need one place to simplify and bring together fragmented, complex infrastructure that spans private and public cloud environments. Our global platform allows customers to place infrastructure wherever they need it and connect it to everything they need to succeed. 

At Equinix, we help the world’s digital leaders scale with agility, speed the launch of digital services, deliver world-class experiences, and transform people’s lives. Our culture is based on collaboration and the growth and development of our teams.   

We hire hardworking people who thrive on solving challenging problems and give them opportunities to hone new skills, and try new approaches, as we grow our product portfolio with new software and network architecture solutions. We embrace diversity in thought and contribution and are committed to providing an equitable work environment that is foundational to our core values as a company and is vital to our success. 

Job Summary: 

We are seeking a seasoned AI Engineer with 5-7 years of industry experience and proven expertise in designing, developing, and deploying AI agents and generative AI solutions in production environments. The ideal candidate will own the end-to-end lifecycle of LLM-powered applications and AI-driven features, from problem definition, solution architecture, and model integration to deployment, evaluation, monitoring, and continuous optimization. 

This role sits at the intersection of software engineering and applied AI, requiring a strong ability to translate business requirements into scalable, production-ready solutions. You will work extensively on integrating large language models and AI agents into enterprise applications and services, while ensuring reliability, performance, security, and measurable business impact. Success in this role demands equal strength in building robust software systems and leveraging cutting-edge AI technologies to solve real-world problems. 

Responsibilities:  

• Design, develop, and deploy production-grade LLM, multimodal AI, and generative AI solutions, owning the entire application lifecycle - integration, monitoring, and continuous optimization. 

• Implement, and optimize Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embeddings, vector databases, retrieval strategies, chunking methodologies, and prompt engineering, with clearly defined and measurable quality metrics. 

• Integrate AI models and agents into enterprise applications, backend services, and APIs, ensuring scalability, reliability, security, cost efficiency, and low-latency performance in production environments. 

• Collaborate closely with product, engineering, delivery, and business stakeholders to translate business requirements into AI-driven solutions, ensuring technical decisions align with strategic objectives and deliver measurable business outcomes. 

• Drive continuous improvement of AI systems through experimentation, performance analysis, user feedback, and emerging advancements in generative AI technologies and frameworks. 

Qualifications: 

• 5-7 years of software engineering experience, including 2–3 years of hands-on experience designing, deploying, and operating AI/ML solutions in production environments. 

• Strong proficiency in Python and solid software engineering fundamentals, including data processing, API development and integration, distributed systems, and model serving.  

• Proficiency in Postgres SQL is required; experience with Java, Node.js, or Scala is a plus. 

• Familiarity with Model Context Protocol (MCP) and experience building, integrating, or consuming MCP servers, tools, and agent ecosystems. 

• Demonstrated experience working with Large Language Models (LLMs), multimodal AI, and generative AI technologies, including agentic workflows, Retrieval-Augmented Generation (RAG), prompt engineering, and AI application development. 

• Hands-on expertise with modern AI/ML frameworks and orchestration tools, such as PyTorch, LangChain, LangGraph, LlamaIndex, or equivalent technologies. 

• Working experience with cloud-native AI platforms, with a strong preference for AWS services (e.g., Bedrock, SageMaker).  

• Proven track record of delivering AI-driven solutions that generate measurable business value, improve operational efficiency, or enhance customer experiences. 

• Excellent collaboration, stakeholder management, and communication skills, with the ability to work effectively across engineering, product, delivery, and business teams. 

• Demonstrated ability to work with a high degree of ownership, accountability, and attention to detail while managing complex, multi-component AI systems in dynamic environments. 

Nice to Have 

• Experience with containerization, deployment, and inference serving technologies, including Docker, FastAPI, Kubernetes, vLLM, Ollama, or similar tools. 

• Ability to visualize, interpret, and communicate AI-generated insights through modern analytics platforms, dashboards, and reporting tools. 

• Contributions to AI/ML open-source projects, research initiatives, or production-scale AI innovation programs are highly desirable. centres