Staff Engineer - Machine Learning

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.

We are seeking a Machine Learning Staff Engineer to lead the development of core backend services powering our Agentic AI Platform. In this role, you will be the primary technical driver for building reasoning-driven agents, multi-agent orchestration, and outcome-based workflows.

You will collaborate with the Principal AI Architect to bridge the gap between high-level design and scalable implementation. You will own the development of high-performance APIs and orchestration runtimes, integrating frameworks like Lang Chain, Lang Graph, and Lang Smith. This role is highly hands‑on and requires a technical leader who can navigate a fast‑paced environment while maintaining high engineering standards.

• Lead Implementation:
  Act as the lead engineer for agent runtime orchestration services, focusing on reasoning, planning, and tool invocation
• API & SDK Design:
  Own the development of robust APIs and SDKs that enable internal and external teams to build and deploy agent workflows
• State & Memory Management:
  Build and optimize stateful dialog management and memory services for multi‑turn, context‑aware agents
• Agent Communication:
  Implement multi‑agent communication (A2A) protocols and shared context systems

• Microservices Leadership:
  Execute the transition to cloud‑native, event‑driven backend services using microservices or service mesh architectures
• Workflow Design:
  Build and manage task scheduling and long‑running operations using tools like Temporal or Airflow
• Performance Optimization:
  Optimize LLM orchestration for performance and cost through caching, batching, and token monitoring

• Data Pipeline Ownership:
  Implement and maintain RAG pipelines and integrations with vector databases (e.g., Pinecone, Weaviate, FAISS)
• Telemetry Integration:
  Ensure all backend services support Lang Smith‑based evaluation and comprehensive observability

• Tenant Isolation:
  Implement RBAC, API authentication, and multi‑tenant isolation logic
• Engineering Excellence:
  Set the standard for automated testing (unit, integration, load) and implement Open Telemetry for full system visibility
• Mentorship:
  Provide technical guidance and code reviews for more junior backend engineers on the team

• 6+ years of backend engineering experience
• 1‑2+ years of hands‑on experience in AI/ML platform development
• Expert Proficiency:
  Strong programming skills in Java and Python
• Framework Expertise:
  Hands‑on experience with Lang Chain, Lang Graph, or Lang Smith
• Distributed Systems:
  Deep understanding of event‑driven architectures, microservices, and Kubernetes
• Cloud & Data:
  Solid experience with AWS, Terraform, and a mix of relational (PostgreSQL) and Vector databases
• Orchestration:
  Practical experience with workflow engines like Temporal or Airflow and messaging systems like Kafka

• Experience with AI‑driven planning systems or dialog state tracking
• Background in enterprise security (RBAC, SSO, OAuth2)
• Experience working in a…