Principal Engineer - Machine Learning

About Freshworks

Organizations everywhere struggle under the crushing costs and complexities of “solutions” that promise to simplify their lives. To create a better experience for their customers and employees. To help them grow. Software is a choice that can make or break a business. Create better or worse experiences. Propel or throttle growth. Business software has become a blocker instead of ways to get work done.

There’s another option. Freshworks. With a fresh vision for how the world works.

Freshworks Inc. builds uncomplicated service software that delivers exceptional employee and customer experiences. Our people-first approach to AI eliminates friction, helping businesses reduce complexity, lower cost-to-serve, and deliver faster, more human support through enterprise-grade yet easy-to-use CX and IT solutions. Nearly 75,000 companies, including Bridgestone, New Balance, Nucor, S&P Global, and Sony Music, trust Freshworks to power their Employee Experience (EX) and Customer Experience (CX) operations.

Fresh vision. Real impact. Come build it with us.

About the Role: Freshworks is building next-generation AI-driven customer and employee engagement platforms. As a Principal Engineer - Machine Learning on our AI/ML Engineering team, you will drive the architecture and technical strategy for core backend services powering our Agentic AI Platform. You will lead the development of reasoning-driven agents, multi-agent orchestration, and outcome-based workflows that directly power autonomous AI capability across our suite of enterprise products. This role is pivotal in establishing engineering standards, scaling backend infrastructure, and mentoring senior engineers as we expand our agentic capabilities at enterprise scale.

What You'll Do

• Drive Platform & Runtime Architecture: Lead the architectural design and execution of agent runtime orchestration services, focusing on reasoning, planning, tool invocation, and low-latency execution.

• Define API & SDK Standards: Design and establish robust, developer-friendly APIs and SDKs that enable internal engineering teams and external ecosystem partners to build, deploy, and manage complex agent workflows.

• Architect Stateful & Memory Systems: Build and optimize stateful dialog management, shared context systems, and memory services for multi-turn, context-aware autonomous agents.

• Implement Multi-Agent Communication Protocols: Establish scalable agent-to-agent (A2A) communication protocols, coordination patterns, and shared memory spaces for distributed multi-agent workflows.

• Lead Cloud-Native Microservices Migration: Drive the transition toward cloud-native, event-driven backend services using modern microservices, service mesh, and event-streaming architectures.

• Optimize LLM Performance & Cost Efficiency: Design and implement LLM orchestration optimizations—including semantic caching, dynamic batching, token monitoring, and intelligent model routing—to ensure high performance and cost governance.

• Own Integrations & Observability: Direct the implementation of enterprise RAG pipelines, vector database integrations (e.g., Pinecone, Weaviate, FAISS), and deep LangSmith/OpenTelemetry tracing for end-to-end evaluation and visibility.

• Drive Technical Excellence & Mentorship: Establish rigorous standards for automated testing (unit, integration, load), tenant isolation (RBAC, multi-tenancy), and provide technical mentorship to elevate engineering capabilities across the team.

Must Have

• Experience: 12–18 years of progressive software engineering and machine learning system development experience, with a track record of architecting scalable enterprise backend systems.

• Technical Mastery: Deep proficiency in Java and Python, with hands-on expertise building production systems using agentic orchestration frameworks (e.g., LangChain, LangGraph, LangSmith) and workflow engines (e.g., Temporal, Airflow).

• Distributed Systems Expertise: Advanced understanding of event-driven architectures, microservices, Kubernetes, Kafka, AWS cloud infrastructure, and mixed database paradigms (PostgreSQL, Vector DBs).

• Execute with Excellence: Proven ability to navigate ambiguity, solve complex architectural bottlenecks, anticipate technical risks, and connect cross-functional dependencies to deliver high-impact engineering initiatives at scale.

• Lead with Vision & Strategy: Demonstrated capability in translating complex business and AI goals into actionable long-term platform roadmaps while influencing technical decisions across cross-functional engineering partners.

• Cultivate a Growth Mindset: A track record of identifying innovative tools, challenging assumptions, and driving technical continuous improvement across the broader engineering organization.

Nice to Have

• Experience with AI-driven planning systems, dialog state tracking, or reinforcement learning feedback loops in production environments.

• Hands-on experience with enterprise security frameworks, including SSO, OAuth2, RBAC, and multi-tenant data isolation.

• Active involvement in open-source AI/ML projects or contributions to major LLM/orchestration libraries.

Please note this is a hybrid role that requires an in-office presence 3 days / week (Tue-Thu).