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Staff Agentic AI Engineer

Equinix

Bengaluru

3+ years

Today

$26.5K–41.0K/yr

Full-time

Hybrid

Skills Required

LLM

Prompt Engineering

Retrieval

OpenAI

Embeddings

Context Engineering

Tool Calling

Multi-agent Workflows

MCP

A2A

Bedrock

Vertex

LangGraph

Enterprise Knowledge Graphs

Git

Description

Equinix is hiring a Staff Agentic AI Engineer to build production AI agents and the platform they run on. The role focuses on agentic delivery across the software lifecycle, with humans directing the work and owning every gate.

Company: Equinix

Role: Staff Agentic AI Engineer

Location: Hybrid, Bengaluru, Karnataka, India

Experience

- 3+ years of qualified software engineering experience
- Record of shipping and operating production systems
- Hands-on experience building LLM-powered applications or agents
- Experience designing evaluations for AI systems, or strong test-engineering instincts for non-deterministic software
- Strong proficiency in Python or TypeScript
- Fluency with modern engineering practice: Git, automated testing, CI/CD, observability, and cloud platforms
- Sound judgment about when to trust automation and when to demand human review
- Communication skills to explain that reasoning

Responsibilities

- Design, build, and ship LLM-powered agents for intake triage, estimation, requirements, technical design, coding, testing, release, and operations
- Engineer scaffolding for dependable agents using MCP tool use, A2A handoffs, event-driven orchestration, and Jira and enterprise system integration
- Build on Equinix's enterprise AI platform with AI gateway, orchestration, audit, and access control
- Design and automate eval suites for agent output quality on every change
- Make passing evals the release gate for agents




- Define guardrails, human-in-the-loop approval points, review thresholds, and escalation paths
- Instrument agent behavior end to end across quality, latency, cost, and adoption
- Tune prompts, context, and configurations to improve outcomes
- Build knowledge layers over process libraries, decision histories, code, and delivery data
- Establish reusable prompt patterns, context standards, and agent configurations
- Own agents through their full lifecycle, including instructions, context freshness, performance monitoring, feedback, and retirement
- Contribute to the orchestrator, persona consoles, and dashboards for agent-led delivery
- Dogfood agents to build agent systems and feed learnings back into the platform
- Apply strong engineering craft in architecture, code quality, testing, CI/CD, and cloud-native design
- Deliver complete agents and platform components within established patterns
- Own evals and quality end to end
- Set patterns for hardest and most ambiguous problems in orchestration, eval design, and agent reliability at scale
- Define standards others follow and multiply the team

Additional Responsibilities

- Help define how AI-first engineering works at Equinix
- Explain to executives what an agent did, why, and how it is known
- Make the platform simpler, faster, and cheaper as it scales
- Use agents to build agent systems and continuously improve the platform

Nice To Have





- Experience with agent frameworks and protocols such as MCP, A2A, Anthropic or OpenAI APIs, Bedrock, Vertex, or LangGraph
- Experience building developer platforms, orchestration systems, or SDLC tooling with Jira, GitHub, or ServiceNow integration
- Knowledge-engineering experience: retrieval systems, embeddings, or enterprise knowledge graphs
- Experience taking AI features through security, privacy, and responsible AI review in an enterprise
- Evidence of craft such as open-source contributions, technical writing, or internal platforms with devoted users

More Skills LLM-powered agents, Python, TypeScript, API design, microservices, event-driven architecture, automated testing, CI/CD, observability, cloud platforms, Anthropic APIs, OpenAI APIs, Jira, GitHub, ServiceNow, security, privacy, responsible AI, AI observability, cloud-native engineering

Prepare for this role

Recommended resources to build the skills for this position. Sponsored.

Top 50 Embeddings Interview Questions

Zenaique

Curated embeddings questions covering models, similarity, and retrieval.

Top 15 Embeddings Interview Questions (Quick)

Zenaique A shorter embeddings interview prep set for quick review.

Top 50 Model Context Protocol (MCP) Questions

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Curated MCP interview questions from Zenaique.

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