Principal AI Software Development Engineer

F5; Hyderabad, Bengaluru; On-site; INR 3,800,000 - 7,000,000; Full time

Job summary

F5 is seeking a Principal AI Software Development Engineer to act as the technical authority for agentic AI development and high-code agent architectures. You will design scalable agent orchestration, establish enterprise standards, and enable teams to deploy production-ready agents using Gemini, Vertex AI, and internal AI infrastructure.

The role demands deep distributed systems expertise, strong coding in Python and at least one of Go/Java/TypeScript, and experience with LLM-based systems and

Qualifications

• 10+ years of software engineering experience with distributed systems.
• Proficient in Python and one of Go/Java/TypeScript.
• Production experience with LLM-based systems and tool usage.
• Experience with Vertex AI, Gemini APIs, or OpenAI APIs.
• Strong API design, microservices, Kubernetes, and cloud-native architectures.
• Experience building or integrating orchestration frameworks.
• Familiarity with vector databases and embedding pipelines.
• Security, auth, and enterprise pattern knowledge.
• Ability to build reusable platforms, not point solutions.

Responsibilities

• Design and implement enterprise-grade agent orchestration frameworks.
• Establish patterns for multi-agent collaboration, event-driven execution, and workflow chaining across enterprise systems.
• Define standards for agent lifecycle management, state persistence, and context engineering.
• Lead technical integration of Gemini models via Vertex AI, ensuring secure, scalable API consumption and proper model routing.
• Develop internal SDKs, abstractions, and reusable components to standardize Gemini usage across F5 teams.
• Optimize prompt engineering, token efficiency, grounding strategies, and structured output patterns.
• Build reference implementations and reusable frameworks for high-code agents in Java, Python, Go, or TypeScript.
• Establish secure integration patterns for agents interacting with Salesforce, Snowflake, SharePoint, ServiceNow, and internal APIs.
• Drive best practices for MCP (Model Context Protocol) server development and secure API mediation.
• Implement logging, tracing, telemetry, and evaluation pipelines for agent performance and reliability.
• Establish guardrails including input/output validation, hallucination mitigation, prompt injection defenses, and policy enforcement.
• Partner with Security to ensure secure data handling, RBAC enforcement, and compliance alignment.
• Support engineering teams adopting Gemini Code Assist, CLI workflows, and internal AI development platforms.
• Create technical documentation, internal libraries, and code samples for no-code, low-code, and pro-code agent builders.
• Provide architectural review and guidance for AI-enabled applications across F5.
• Optimize inference latency, parallelization, and cost management strategies across agent workflows.
• Implement caching strategies, streaming responses, and batching techniques to improve throughput and reliability.
• Evaluate and benchmark agent/model performance across different workloads.

Skills

Distributed systems

Python

Go

Java

TypeScript

LLM-based systems

API design

Kubernetes

Security patterns

Tools

Vertex AI

Gemini APIs

OpenAI APIs

LangChain

LlamaIndex

Snowflake

Salesforce

SharePoint

Job description

Principal AI Software Development Engineer

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Role Summary

F5 Digital is building an enterprise-scale Agentic AI platform to enable secure, observable, and production-grade AI agents across the organization. We are seeking a Principal AI Software Development Engineer to serve as the technical authority and hands‑on architect for high‑code agent development, orchestration frameworks, and enterprise AI integration during the Gemini rollout.

This is a deeply technical individual contributor role. The Principal Engineer will design and implement scalable agentic systems, establish engineering standards for AI workflows, and enable engineering teams to build production‑ready agents using Gemini, Vertex AI, and internal AI infrastructure.

The ideal candidate has strong distributed systems expertise, deep familiarity with LLM architectures and agent frameworks, and the ability to translate AI theory into secure, production‑grade implementations.

Key Responsibilities Agentic Architecture Orchestration

• Design and implement enterprise-grade agent orchestration frameworks supporting tool use, memory, RAG, agentic workflows and automation.
• Establish patterns for multi‑agent collaboration, event‑driven execution, and workflow chaining across enterprise systems.
• Define standards for agent lifecycle management, state persistence, and context engineering.

Gemini Vertex AI Integration

• Lead technical integration of Gemini models via Vertex AI, ensuring secure, scalable API consumption and proper model routing.
• Develop internal SDKs, abstractions, and reusable components to standardize Gemini usage across F5 teams.
• Optimize prompt engineering, token efficiency, grounding strategies, and structured output patterns.

High‑Code Agent Enablement

• Build reference implementations and reusable frameworks for high‑code agents in Java, Python, Go, or TypeScript.
• Establish secure integration patterns for agents interacting with Salesforce, Snowflake, SharePoint, ServiceNow, and internal APIs.
• Drive best practices for MCP (Model Context Protocol) server development and secure API mediation.

Observability, Safety Governance

• Implement logging, tracing, telemetry, and evaluation pipelines for agent performance and reliability.
• Establish guardrails including input/output validation, hallucination mitigation, prompt injection defenses, and policy enforcement.
• Partner with Security to ensure secure data handling, RBAC enforcement, and compliance alignment.

Developer Enablement Technical Leadership

• Support engineering teams adopting Gemini Code Assist, CLI workflows, and internal AI development platforms.
• Create technical documentation, internal libraries, and code samples for no‑code, low‑code, and pro‑code agent builders.
• Provide architectural review and guidance for AI‑enabled applications across F5.

Performance Scalability

• Optimize inference latency, parallelization, and cost management strategies across agent workflows.
• Implement caching strategies, streaming responses, and batching techniques to improve throughput and reliability.
• Evaluate and benchmark agent/model performance across different workloads.

Required Qualifications

• 10+ years of experience in software engineering, with significant experience in distributed systems and backend architecture.
• Deep hands‑on coding expertise in Python and at least one of: Go, Java, or TypeScript.
• Production experience with LLM‑based systems, including prompt engineering, tool calling, RAG, embeddings, and agent frameworks.
• Experience with Vertex AI, Gemini APIs, OpenAI APIs, or similar enterprise AI platforms.
• Strong understanding of API design, microservices, Kubernetes, and cloud‑native architectures.
• Experience building or integrating orchestration frameworks (e.g., LangChain, LlamaIndex, custom orchestration layers).
• Familiarity with vector databases, embedding pipelines, and retrieval strategies.
• Strong understanding of authentication, authorization, and enterprise security patterns.
• Proven ability to build reusable platforms, not point solutions.

Preferred Qualifications

• Experience building multi‑agent systems or autonomous workflow engines.
• Experience with model evaluation pipelines and AI quality metrics.
• Familiarity with structured output enforcement (JSON schemas, function calling).
• Experience working with enterprise data systems such as Snowflake, Salesforce, ServiceNow, SharePoint.
• Knowledge of cost modeling and inference optimization techniques.
• Experience contributing to internal developer platforms or SDK ecosystems.
• Background in AI safety, red‑team, or model robustness evaluation.

What Success Looks Like

A successful Principal AI Software Development Engineer in this role will:

• Deliver a secure, scalable orchestration layer for enterprise agents.
• Enable engineering teams to build production‑grade high‑code agents with consistent architecture patterns.
• Establish strong observability, evaluation, and safety controls for AI‑driven workflows.
• Accelerate AI agents rollout by providing reusable integrations, SDKs, and reference implementations.
• Serve as the technical authority for enterprise agentic AI engineering at F5.