18 hours ago
Bengaluru, IndiaStaff+
Responsibilities
- Design, build, deploy, and maintain LLM-powered agents and AI-driven applications.
- Integrate AI capabilities into existing software platforms and develop AI-enabled APIs and backend services.
- Implement Model Context Protocol, agent-to-agent communication, tool integration, and multi-agent workflow orchestration.
- Create automated evaluation frameworks, quality gates, benchmarking processes, release criteria, guardrails, and Responsible AI governance controls.
- Design and maintain RAG architectures, vector database integrations, and knowledge-engineering solutions.
- Monitor and optimize agent quality, accuracy, latency, reliability, adoption, observability, and operational costs.
- Troubleshoot agent, API, integration, client-interface, and end-user issues and improve performance through prompt and context engineering.
- Design, build, and manage automated delivery pipelines using GitHub Actions and modern DevOps practices.
- Provide hands-on leadership in architecture, development, testing, deployment, operational support, and engineering standards.
- Collaborate with product managers, architects, security teams, and engineering leaders on technical strategy, estimates, risks, roadmaps, and execution.
- Evaluate emerging AI technologies and monitor user feedback to drive continuous product improvement.
Requirements
- Bachelor’s degree in computer science, software engineering, or data science.
- 6+ years of full software development life-cycle experience and 6+ years of software design, development, and algorithm-related experience.
- 5+ years of programming experience with Python or Java.
- 3+ years of experience developing, deploying, or optimizing ML models.
- 3+ years of hands-on experience building LLM-powered applications, AI assistants, or autonomous agent systems.
- Experience with prompt engineering, context engineering, tool calling, retrieval systems, multi-agent workflows, APIs, microservices, distributed systems, and event-driven architectures.
- Experience with LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar agent frameworks and orchestration technologies.
- Experience with Model Context Protocol, agent-to-agent communication, and tool orchestration frameworks.
- Experience with Anthropic, OpenAI, Azure OpenAI, Amazon Bedrock, Vertex AI, or similar AI platforms.
- Knowledge-engineering experience with embeddings, hybrid search, retrieval systems, vector databases, and enterprise knowledge graphs.
- Experience implementing AI observability, tracing, evaluation platforms, cost optimization, automated testing, delivery pipelines, and operational monitoring.
- Experience with AWS, Azure, or Google Cloud Platform and REST-based API or client SDK development.
- Strong written and verbal communication skills, ability to explain complex concepts to nontechnical stakeholders, customer focus, independent execution, and comfort with ambiguity.
Tech Stack
Categories
About Equinix
Equinix is a public digital infrastructure company that operates a global platform of colocation and interconnection data centers for enterprises, cloud and network providers. It sells space, power, and connectivity, plus services such as Equinix Fabric, Network Edge, and Equinix Metal for hybrid multicloud and edge deployments. Founded in 1998 and headquartered in Redwood City, California, it is listed on NASDAQ (EQIX) and runs hundreds of IBX facilities across major metros worldwide.