Job Description

About the Role

Anaplan is seeking a strong Tech Lead to join our Predictive Insights team — the group responsible for designing, building, and operating the machine learning systems that deliver classification-driven intelligence across Anaplan's Connected Planning platform. In this role, you will serve as the technical authority for a cross-functional squad of Backend, Frontend, and QA engineers, guiding everything from model design and data engineering to production deployment and observability.

You will own the end-to-end lifecycle of classification models and ML pipelines: from prototyping in Python and feature-store integration, through containerised deployment on Kubernetes, to real-time serving backed by Redis and MongoDB. As a hands-on leader you will set architectural standards, mentor engineers at all levels, and work closely with Product and Data Science to translate customer needs into reliable, scalable predictive capabilities.

Key Responsibilities

Technical Leadership

• 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 the team.

• 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, ensuring quality, performance, and security across all layers.

Team Leadership & People Management

• 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.

ML Pipelines & Classification Models

• 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.

Infrastructure & Platform

• 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.

Cross-Functional Collaboration

• 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.

Required Qualifications

Machine Learning & Python

• Python proficiency: 7+ years of production Python development; expert-level use of scikit-learn, Pandas, NumPy, and at least one deep learning framework (PyTorch or TensorFlow).

• Classification models: Deep hands-on experience building and deploying classification systems, including feature selection, imbalanced-class handling, calibration, and explainability (SHAP, LIME).

• ML pipelines: Proven ability to design, build, and maintain end-to-end ML pipelines in production, including orchestration (Airflow, Prefect, or similar).

• MLOps: Strong command of model lifecycle management — versioning, registry, A/B testing, canary deployments, monitoring, and alerting.

Infrastructure & Data Technologies

• Kubernetes (k8s): Hands-on experience deploying and operating containerised applications on Kubernetes; familiar with Helm, resource quotas, HPA, and service mesh fundamentals.

• MongoDB: Solid experience with document-oriented data modelling, query optimisation, aggregation pipelines, and schema evolution strategies.

• Redis: Experience with Redis as a caching layer, real-time feature store, and/or message broker (Streams or pub/sub).

• Cloud platforms: Comfortable with at least one major cloud provider (AWS, Azure, or GCP) and related managed services (object storage, managed databases, container registries, monitoring).

• CI/CD: Strong grasp of DevOps practices — Docker, GitHub Actions or similar, automated testing, and GitOps workflows.

Leadership & Communication

• 3+ years of experience leading cross-functional engineering teams, ideally including Backend, Frontend, and QA disciplines.

• Track record of delivering complex, multi-team projects on time and with high quality.

• Excellent written and verbal communication skills in English; able to represent technical decisions to non-technical stakeholders.

• Experience working effectively in a globally distributed organisation across multiple time zones.

Preferred Qualifications

• Master's or Ph.D. in Computer Science, Statistics, or a closely related quantitative field.

• Experience with probabilistic classification and uncertainty quantification (calibrated probabilities, conformal prediction).

• Familiarity with large-scale data processing frameworks such as Apache Spark or Dask.

• Prior exposure to SaaS or enterprise platform environments, particularly in finance, supply chain, or planning domains.

• Experience with additional data stores relevant to the predictive stack: Elasticsearch, Cassandra, or similar.

• Contributions to open-source ML tooling or published research in applied ML.

Core Technology Stack

Languages

Python (primary), SQL, Bash; JavaScript/TypeScript (awareness)

ML / AI

scikit-learn, XGBoost, LightGBM, PyTorch, MLflow, SHAP

Pipelines

Apache Airflow / Prefect, Docker, Kubernetes (Helm, HPA)

Data Stores

MongoDB, Redis, PostgreSQL; cloud object storage (S3 / Blob)

Cloud

AWS / Azure / GCP — managed services, IAM, monitoring

CI/CD

GitHub Actions, Docker, container registries, GitOps

Observability

Prometheus, Grafana, distributed tracing, model monitoring

What Makes This Role Exciting

You will sit at the intersection of applied machine learning and platform engineering, building the predictive intelligence layer that thousands of enterprise planning teams rely on daily. The Predictive Insights team operates with significant technical autonomy, and as its Tech Lead you will have the rare opportunity to shape both the engineering culture and the product direction from within India — working in tight collaboration with global counterparts across the US and Europe.

You will lead a diverse team where Backend, Frontend, and QA perspectives are all valued equally, and where engineering rigour and customer impact are the twin north stars. If you are excited by the challenge of making classification models production-ready at enterprise scale, and by growing the next generation of ML engineers, this is the role for you.

About Anaplan

Anaplan is a cloud-native Connected Planning platform used by more than 2,000 of the world's leading organisations to drive better decisions across finance, supply chain, sales, and HR. Headquartered in San Francisco and with engineering hubs across the US, Europe, and India, Anaplan is on a mission to turn complex business data into actionable insight — at scale.