Manager, Software Development

Job Overview

Role: Manager, Software Development. Department: Product and Engineering. Location: Gurugram, India.

Eligibility / Qualification Required:

At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market.
What unites Anaplanners across teams and geographies is our collective commitment to our customers’ success and to our Winning Culture.
Our customers rank among the who’s who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform.
Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebratingour wins – big and small.
Supported by operating principles of being strategy-led, values-based and disciplined in execution, you’ll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let’s build what’s next - together!
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. 
Our Commitment to Diversity, Equity, Inclusion and Belonging (DEIB)
We believe attracting and retaining the best talent and fostering an inclusive culture strengthens our business. DEIB improves our workforce, enhances trust with our partners and customers, and drives business success. Build your career in a place where diversity, equity, inclusion and belonging aren’t just words on paper – this is what drives our innovation, it’s how we connect, and it contributes to what makes us a market leader. We believe in a hiring and working environment where all people are respected and valued, regardless of gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, disability status, citizenship, or any other aspect which makes people unique. We hire you for who you are, and we want you to bring your authentic self to work every day! 
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive equitable benefits and all privileges of employment. Please contact us to request accommodation.  
Fraud Recruitment Disclaimer  
It has come to our attention that fraudulent and fictitious job opportunities are being circulated on the Internet. Prospective candidates are being contacted by certain individuals, mainly through telephone calls, emails and correspondence, claiming they are representatives of Anaplan. The main purpose of these correspondences and announcements is to obtain privileged information from individuals.  
Anaplan does not:  

• Extend offers to candidates without an extensive interview process with a member of our recruitment team and a hiring manager via video or in person.   
• Send job offers via email. All offers are first extended verbally by a member of our internal recruitment team whenever possible and then followed up via written communication.  

All emails from Anaplan would come from an @anaplan.com email address. Should you have any doubts about the authenticity of an email, letter or telephone communication purportedly from, for, or on behalf of Anaplan, please send an email to people@anaplan.com before taking any further action in relation to the correspondence.   
 

How to Apply:

Apply online through the official Anaplan Greenhouse job page.
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