Job Overview
Location
Gurugram, India
Job Type
Full-time
Category
Data Science
Date Posted
May 25, 2026
Full Job Description
📋 Description
- • Lead the design, development, and deployment of production-grade ML pipelines for classification tasks including binary, multi-class, and multi-label models.
- • Define the end-to-end ML lifecycle: data ingestion, feature engineering, model training, evaluation, deployment, and continuous monitoring.
- • Establish coding standards, design-review practices, and engineering excellence benchmarks for a cross-functional squad of Backend, Frontend, and QA engineers.
- • Drive architectural decisions across the full stack, including Python services, containerised workloads on Kubernetes, and data-layer integrations with MongoDB and Redis.
- • Conduct rigorous code reviews and technical deep-dives to ensure quality, performance, and security across all layers of the ML system.
- • Manage and mentor a diverse team comprising Backend Engineers, Frontend Engineers, and QA Engineers, adapting leadership style to each discipline.
- • Own individual growth plans, performance feedback cycles, and career development conversations for direct reports.
- • Foster a culture of psychological safety, continuous learning, and technical curiosity across a geographically distributed team.
- • Partner with Engineering Managers and cross-functional leads to balance workloads, remove blockers, and maintain delivery velocity.
- • Actively recruit, interview, and onboard strong engineering talent in India.
- • Architect and maintain scalable ML pipelines from raw data processing through to model serving using Python-first tooling (scikit-learn, XGBoost, LightGBM, PyTorch).
- • Own model selection, hyperparameter tuning, cross-validation strategies, and performance benchmarking for classification workloads.
- • Integrate feature stores and ensure reproducible, version-controlled experiments using tools such as MLflow or similar.
- • Define and enforce model monitoring, drift detection, and retraining policies in production.
- • Collaborate with Data Scientists to operationalise research prototypes into robust, maintainable services.
- • Lead adoption of Kubernetes (k8s) best practices: pod design, resource management, horizontal scaling, health checks, and rolling deployments for ML services.
- • Design and optimise data access patterns in MongoDB (document modelling, indexing, aggregations) and Redis (caching strategies, pub/sub, and session management).
- • Work with DevOps/Platform teams to maintain CI/CD pipelines, containerisation standards, and cloud infrastructure (AWS, Azure, or GCP).
- • Ensure high availability, fault tolerance, and performance SLAs for prediction-serving APIs.
- • Partner with Product Management to define technical roadmap and translate business requirements into engineering plans.
- • Collaborate with the QA lead to design comprehensive test strategies — unit, integration, and model validation — covering both software and ML components.
- • Interface with Frontend Engineers to specify clean, versioned prediction APIs and ensure seamless UX integration.
- • Contribute to and review technical RFCs and architecture decision records (ADRs) across the broader Engineering organisation.
- • Operate with technical autonomy to shape both engineering culture and product direction from India while collaborating with global counterparts in the US and Europe.
- • Build the predictive intelligence layer that thousands of enterprise planning teams rely on daily, with engineering rigour and customer impact as twin north stars.
🎯 Requirements
- • 7+ years of production Python development with expert-level use of scikit-learn, Pandas, NumPy, and at least one deep learning framework (PyTorch or TensorFlow)
- • Deep hands-on experience building and deploying classification systems including feature selection, imbalanced-class handling, calibration, and explainability (SHAP, LIME)
- • Proven ability to design, build, and maintain end-to-end ML pipelines in production including orchestration (Airflow, Prefect, or similar)
- • Strong command of MLOps practices including model versioning, registry, A/B testing, canary deployments, monitoring, and alerting
- • Hands-on experience deploying and operating containerised applications on Kubernetes with familiarity with Helm, resource quotas, HPA, and service mesh fundamentals
- • 3+ years of experience leading cross-functional engineering teams including Backend, Frontend, and QA disciplines
🏖️ Benefits
- • Opportunity to lead a diverse, globally distributed team with significant technical autonomy
- • Work on a platform used by over 2,400 global enterprises including Coca-Cola, Adobe, LVMH, and Bayer
- • Collaborate with global counterparts across the US and Europe from an engineering hub in India
- • Contribute to shaping engineering culture and product direction within a market-leading Connected Planning platform
- • Be part of a company that champions diversity of thought, psychological safety, and inclusive culture
- • Access to a technology stack including Python, Kubernetes, MongoDB, Redis, AWS/Azure/GCP, and MLflow
Skills & Technologies
Python
JavaScript
TypeScript
PostgreSQL
MongoDB
Hybrid
Degree Required
About Anaplan, Inc.
Anaplan, Inc. provides a cloud-native, enterprise-grade planning and performance-management platform that connects financial, operational, and line-of-business data in real time. Its proprietary Hyperblock calculation engine enables multidimensional modeling, scenario testing, and continuous forecasting across sales, supply chain, workforce, and finance use cases. Organizations use Anaplan to replace fragmented spreadsheets, accelerate budgeting cycles, align resources with demand, and monitor KPIs through interactive dashboards. The subscription SaaS supports no-code app building, workflow automation, and role-based collaboration, integrating via APIs with ERP, CRM, HRIS, and BI systems. Founded in 2006, the company is headquartered in San Francisco and serves global Fortune 2000 clients.
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