Responsibilities
Develop and deploy production-grade LLM, multimodal AI solutions, owning the entire application lifecycle - integration, monitoring, and continuous optimization
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
1-4 years of software engineering experience, including 1-2 years of hands-on experience in building and deploying 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) including agentic workflows, Retrieval-Augmented Generation (RAG), prompt engineering, and AI application development
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
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
Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability. If you are a qualified candidate and need assistance or an accommodation, please let us know by completing this form.
Equinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.
- We use artificial intelligence in our hiring process. Learn more here.
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This posting is for a backfill position, meaning it is to fill an existing vacancy within our organization.

