Description
As a Staff Machine Learning Engineer at Freshworks, you will serve as the critical architectural bridge between cutting-edge Data Science research and massive-scale, product-ready implementation.
Your work will directly result in:
- Massive Scale & Reliability: Architecting and deploying robust ML APIs and pipelines capable of serving millions of requests with ultra-low latency and unwavering reliability.
- Engineering Excellence: Setting the gold standard for ML Engineering practices, MLOps, and system design across the organization.
- Accelerated AI Innovation: Transforming theoretical models into high-performance, production-grade systems, directly shrinking the time-to-market for complex ML business solutions.
- Cross-Organizational Multiplier: Acting as a strategic technical anchor, influencing cross-product architects, leading POCs, and mentoring teams to ensure tight technical alignment across all engineering groups.
Responsibilities:
- End-to-End Pipeline Architecture: Architect, build, and manage comprehensive, highly scalable ML pipelines covering data pre-processing, model generation, automated deployment, cross-validation, and active feedback loops.
- ML Algorithm Implementation: Partner deeply with Data Scientists to translate complex, theoretical ML models and algorithms into high-performance, production-grade code.
- High-Performance Service Delivery: Design, develop, and deploy highly extensible ML API services rigorously optimized for low latency and massive scalability.
- Operational Intelligence & Observability: Devise and build advanced monitoring capabilities to track both engineering system health and ML model performance metrics (drift, accuracy, etc.) over the long term.
- Strategic Innovation & Architecture: Architect solutions from scratch, leading Proof of Concept (POC) initiatives across various tech stacks to validate optimal solutions for complex business challenges.
- Technical Leadership & Execution: Own the full lifecycle of feature delivery autonomously,from requirement gathering with product stakeholders to final deployment,while collaborating with cross-product architects to drive platform adoption.
Qualifications:
- Experience: 9+ years of progressive, highly relevant experience in software engineering and machine learning development.
- Production Excellence: A proven, demonstrable track record of successfully architecting, building, and productionizing complex Machine Learning solutions at an enterprise scale.
- MLOps Mastery: Deep, practical experience with modern MLOps practices, ensuring seamless, automated, and secure model transitions from development and training into production environments.
- Education: A Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Mathematics, or a related quantitative field.
Skills:
- Core Programming: Expert-level Object-Oriented Programming (OOP) expertise in Python and Java.
- ML & Deep Learning Frameworks: Mastery of industry-standard ML libraries and Deep Learning frameworks, including PyTorch, Keras, TensorFlow, and TFServing.
- Foundational Engineering: Deep, advanced understanding of Data Structures, Algorithms (DSA), and complex, distributed System Design.
- Cloud & Infrastructure: Hands-on knowledge and proficiency with Cloud infrastructure and services (AWS highly preferred) for large-scale data processing and ML model hosting.
- Analytical Problem Solving: Exceptional analytical and debugging skills with a relentless focus on algorithmic optimization, resource efficiency, and resolving bottlenecks in distributed systems.