Top Python Developer Jobs in Delhi

Lead the architecture and implementation of a distributed data platform for microscopy, ensuring scalability and high-performance in data management.

As a Senior SDET, you will enhance testing practices, improve CI pipelines, mentor engineers, and ensure platform reliability through comprehensive testing strategies.

Provide DevOps engineering and production support across GCP and OCI: manage cloud infrastructure, build/maintain CI/CD pipelines, implement IaC (Terraform), run containerized workloads (Docker/Kubernetes), monitor and troubleshoot production systems, handle incident management/on-call, ensure security/compliance, and drive automation and reliability improvements.

Design and implement Databricks solutions, develop data pipelines, optimize performance, ensure security, and lead technical initiatives.

As an Associate Automation Engineer, you will write automation scripts for mobile applications, conduct data analysis, maintain testing frameworks, identify bugs, and assist customers. Requires knowledge of Appium, Python, and software QA methodologies.

Seeking a Lead SDET to manage quality strategy, lead a testing team, and ensure high product quality through automation and manual testing.

As a Backend Engineer, you will develop automation frameworks, resolve bugs, optimize performance, and collaborate with teams to enhance product quality.

Senior individual-contributor responsible for designing, building, and scaling enterprise compute, virtualization, container, and storage platforms. Lead infrastructure architecture, automation (IaC), incident response, and cross-team technical direction. Mentor engineers, reduce operational toil through code and automation, evaluate AI-assisted tooling, and ensure resilient, cost-effective hybrid cloud and data center solutions.

Design, train, validate, deploy and monitor ML models (pricing, dispatch, fraud, personalization). Perform EDA, feature engineering, statistical analysis, production-quality Python/SQL development, collaborate with product and engineering, and drive data quality, documentation, and knowledge sharing across the Data & AI domain.

Design, implement, and maintain data pipelines using Elasticsearch and data warehousing solutions, integrate data sources, and ensure data accuracy while collaborating with cross-functional teams.

Design, build, and optimize high-volume data pipelines using Python, Databricks and Spark on Azure. Create and maintain automation test suites (Azure DevOps), perform manual/load/exploratory testing, collaborate with BAs and developers to meet sprint goals, and assist with sprint estimation and delivery.

Design, build, and deploy AI-powered legal applications using LLMs, RAG, and agentic AI. Partner with legal stakeholders to translate workflows, build document ingestion and vector search pipelines, ship production-grade features, implement governance/security controls, and mentor/legal users while aligning solutions with enterprise architecture and monitoring quality and evaluation metrics.

Design, build, and operate scalable data engineering services and ingestion pipelines (CDC/streaming). Develop and optimize a centralized analytics warehouse, data models, and aggregation strategies. Build APIs, enforce multi-tenant security and data governance, monitor pipeline health, improve automation and observability, and ensure production reliability through strong engineering practices.

Support multiple departments with operational tasks: data entry, validation, reporting, basic analysis, research, and cross-team coordination. Maintain datasets, assist with Excel/Sheets reporting, suggest process improvements, and learn AI-powered workflows while working remotely on CET hours.

Design and implement AI models, operationalize ML models, develop CI/CD pipelines for ML workflows, and provide technical guidance while ensuring data security compliance.

Lead application product testing across cloud platforms and Kubernetes, focusing on test automation, performance optimization, and collaboration with cross-functional teams.

Early-career DevSecOps role focused on securing and automating Binance infrastructure: design/deploy AWS security measures, harden systems, analyze logs for threats, perform access and vulnerability assessments, and build or leverage LLMs for anomaly detection.

Design and develop features for the SiConic semiconductor validation platform, implement and optimize test execution, DFT integration, and automation. Develop and debug test flows and integrate components, collaborate cross-functionally, troubleshoot software/hardware interfaces, contribute to architecture and prototypes, and support validation tooling and automation frameworks.

Build and scale production-grade AI/LLM capabilities across backend services: design agentic workflows, implement RAG pipelines, develop secure AI APIs, enforce safety/compliance, enable observability and evaluation, optimize reliability and cost, and contribute to CI/CD, IaC, testing, documentation, and cross-team delivery.

Lead process discovery and translate business needs into requirements. Design, configure, deploy, and maintain AI-driven workflows and agents using low-code/no-code platforms. Support UAT, RPA implementations, cross-platform automations, and stakeholder coordination while recommending technology improvements and ensuring integration with enterprise systems.

Lead a two-month project to assess the current data environment and design, build, and optimize a scalable cloud data warehouse. Deliver automated ETL/ELT pipelines, dimensional models, analytics-ready datasets, documentation, and handover while partnering with analysts and stakeholders for BI and reporting.

Lead cross-functional ML initiatives: choose modeling approaches, define business-linked evaluation metrics, validate with synthetic and real data, deliver MLOps-enabled models on AWS, ensure reproducibility, compliance, cost optimization, and incremental value within short agile release cycles.

Lead the design and development of scalable full-stack web applications and distributed systems, guiding engineering teams and ensuring best practices in software development.

Design, develop, and deploy data engineering solutions and analytics pipelines on Databricks. Build data models, optimize SQL and DAX queries, manage ETL and real-time ingestion, tune performance for large data volumes, and produce dashboards and reports while cross-training team members.

Lead cross-functional delivery of ML and analytics solutions: select modeling approaches, define business-aligned evaluation metrics, validate with synthetic and historical data, implement MLOps on AWS, ensure reproducibility, cost optimization, and regulatory explainability, and ship incremental value in short agile release cycles.