About this role
As a Lead Engineer for the ML Engineering development team, you will act as the critical link between Data Science research and product-ready implementation. Your primary focus is to architect, design, and implement optimal solutions for a wide array of ML use cases. You will partner with Data Scientists to bring sophisticated algorithms to life, building and deploying robust APIs and pipelines that serve millions of requests with high efficiency and reliability.
Key Responsibilities
ML Algorithm Implementation: Collaborate closely with Data Scientists to translate theoretical models and algorithms into high-performance, production-grade code.
End-to-End Pipeline Architecture: Build and manage comprehensive ML pipelines, including data pre-processing, model generation, automated deployment, cross-validation, and active feedback loops.
High-Performance Service Delivery: Develop and deploy extensible ML API services optimized for low latency and high scalability.
Operational Intelligence: Devise and build monitoring capabilities for both engineering efficiency and ML model performance metrics to ensure long-term system health.
Strategic Innovation & Collaboration: Architect solutions from scratch and liaise with cross-product architects and engineers to drive adoption and ensure technical alignment across the organization.
Prototyping & POC Execution: Lead Proof of Concept (POC) initiatives on various tech stacks to identify and validate optimal solutions for complex business challenges.
Independent Execution: Own the full lifecycle of feature delivery, from requirement gathering with product teams to final deployment and monitoring
Professional Qualifications
Experience: 9+ years of relevant experience in software engineering and machine learning development.
Production Excellence: Proven track record in building and productionizing Machine Learning solutions at scale.
MLOps Mastery: Deep experience with MLOps practices, ensuring seamless model transitions from development to production.
Education: Degree in Computer Science, Artificial Intelligence, or a related quantitative field.
Technical Skill Inventory
Programming Core: Strong OOPS and programming expertise in Python and Java.
Framework Proficiency: Expertise with ML libraries and Deep Learning frameworks such as PyTorch, Keras, TensorFlow, and TFServ.
Foundational Engineering: Deep understanding of data structures, algorithms, and system design.
Problem Solving: Exceptional analytical skills with a focus on optimization and efficiency.
Cloud Infrastructure (Plus): Hands-on knowledge of AWS services for ML and data processing.
At Freshworks, we have fostered an environment that enables everyone to find their true potential, purpose, and passion, welcoming colleagues of all backgrounds, genders, sexual orientations, religions, and ethnicities. We are committed to providing equal opportunity and believe that diversity in the workplace creates a more vibrant, richer environment that boosts the goals of our employees, communities, and business. Fresh vision. Real impact. Come build it with us.