Freshworks: Principal Engineer - Machine Learning — Hyderabad, TS, India

About Freshworks

Freshworks develops cloud-based software for customer and employee experiences. Its product range includes customer support, sales and marketing automation, chat and IT service management. The employer names Freshdesk, Freshsales, Freshmarketer, Freshservice and Freshchat, supported by shared platform services. These applications address different operational needs, from managing customer conversations and sales activity to helping employees obtain IT services. The business emphasises software designed for end users and straightforward implementation.

The position and its setting

Freshworks lists Principal Engineer - Machine Learning in Hyderabad, TS, India, under reference REF8773L. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Employment type: Full-time
• Employer listing date: 1 October 2026
• Brands: Freshworks

Responsibilities and work described

• 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
• Leadership: 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
• 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
• Architecture: Stateful & Memory Systems: Build and optimize stateful dialog management, shared context systems, and memory services for multi-turn, context-aware autonomous agents
• Implementation: Multi-Agent Communication Protocols: Establish scalable agent-to-agent (A2A) communication protocols, coordination patterns, and shared memory spaces for distributed multi-agent workflows
• Leadership: 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, 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

Essential requirements and candidate background

• 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: 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

Preferred experience and additional strengths

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

Location, schedule and contract conditions

• Please note this is a hybrid role that requires an in-office presence 3 days / week (Tue-Thu)
• Work arrangement: Hybrid
• Advertised location: Hyderabad, TS, India

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another Principal Engineer - Machine Learning vacancy is not an offer for reference REF8773L; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Freshworks

Applications follow the official employer page linked below.

Open the official Freshworks posting below and match the title and reference REF8773L.

Compare your qualifications and experience with the essential conditions for the Hyderabad, TS, India position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• Java
• Kafka
• Kubernetes
• AWS
• APIs
• AI/ML
• LLMs
• ISO
• machine learning
• AI
• communication