Top Python Developer Jobs in Gurgaon

Lead site reliability engineering across system availability, performance, scalability, observability, incident response, automation, CI/CD, and cloud migrations. Architect OpenTelemetry-based monitoring solutions, improve MTTD and MTTR, develop infrastructure tooling, support on-premises-to-cloud migrations, and apply AI-driven operations. Collaborate with globally distributed engineering teams on performance testing, troubleshooting, postmortems, and continuous reliability improvements.

Owns architectural direction across React front end, Python services, APIs, and Databricks/Snowflake data layers. Establishes engineering standards, leads technical design, mentors engineers, resolves escalated technical issues, and partners with leadership on roadmap and resourcing. Guides full-stack tradeoffs and platform expansion across multiple regions while operating in a regulated, compliance-sensitive data environment.

Provide technical leadership for a new MongoDB platform that enables customers to build and deploy AI applications at scale. Design and implement distributed systems infrastructure, shape architecture and development practices, improve performance, scalability, and reliability, conduct code reviews, mentor engineers, and lead complex projects from conception through delivery. Collaborate with research, product, design, and engineering teams in a hybrid Gurugram environment.

Design and deliver end-to-end packet core and 5G core solutions, optimize network performance, collaborate with product, R&D and customer teams, lead transformations, perform protocol and data analysis, and support deployment and knowledge transfer.

Develops and integrates radiating components and passive radiation-pattern structures for multiband base-station antennas. Responsibilities include 3D and 2D electromagnetic simulation, prototype RF measurements, performance optimization, troubleshooting, technical documentation, and supporting the product lifecycle through mass production. The role collaborates with international cross-functional teams and requires relocation to Rosenheim, Germany.

Implement, configure, and support SMAX in cloud-native Kubernetes or OpenShift environments. Provide L2/L3 ITSM production support, resolve incidents within SLA, perform root cause analysis, and support deployments, patches, and upgrades. Manage integrations with enterprise tools and backend components including PostgreSQL, Elasticsearch, and Kafka. Develop SQL queries, reports, dashboards, and KPIs while performing data validation, reconciliation, and basic performance tuning. Drive automation, troubleshooting, and platform improvements.

Leads the design, development, deployment, and support of AI-driven applications and scalable enterprise services. Develops Python applications, SQL Server databases, cloud-native solutions, infrastructure as code, and CI/CD pipelines across Azure and AWS. Integrates AI capabilities and enterprise APIs, contributes to front-end development, monitors production systems, performs code reviews, and troubleshoots reliability and performance issues while collaborating in an Agile environment.

Design, implement, test, and release features for MongoDB Ops Manager and Automation. Own complex projects from design through incremental delivery, troubleshoot large-scale customer deployments, and build distributed, fault-tolerant systems for on-premises and private cloud environments. The role requires strong software engineering fundamentals, independent problem-solving, and experience with multithreaded, asynchronous systems.

Build and scale production-grade agentic AI infrastructure and platform capabilities. Lead architecture, technical design, reliability, scalability, data privacy, and end-to-end delivery. Collaborate with product and innovation teams to define customer solutions, mentor engineers, improve developer experience, and resolve complex technical issues. The role requires backend engineering, cloud infrastructure, APIs, databases, automated testing, infrastructure as code, and CI/CD expertise.

Develop and lead architecture for a scalable AI application platform. Design, implement, test, debug, and optimize distributed systems and cloud services, while improving performance, scalability, reliability, and developer experience. Collaborate across research, product, design, and engineering teams; contribute to code reviews and development practices; mentor engineers; and own complex projects from problem identification through delivery.

Build and operate MongoDB integrations and product capabilities within ISV codebases. Design data models, migrations, synchronization, dual-write and CDC solutions, and production-ready deployments across cloud, Kubernetes, and on-premises environments. Collaborate with partner engineers through implementation, code reviews, documentation, and rollout while addressing performance, scalability, resilience, observability, and security. The role also develops reusable implementation patterns and requires substantial partner-site travel.

Design, develop, test, and maintain scalable backend services, APIs, and microservices. Contribute to system architecture, data modeling, database optimization, monitoring, and production troubleshooting. Own technical projects through planning, deployment, and maintenance while collaborating with cross-functional stakeholders. Mentor junior engineers and participate in code reviews, agile planning, and workflow improvements. Support warehouse, inventory, logistics, fulfillment, and supply chain technology solutions where applicable.

Develop web, desktop, and cloud applications using .NET, Angular, React, Node.js, and Python. Design user interfaces, perform testing and code reviews, translate requirements into software features, and collaborate with global engineering teams. Evaluate and implement cloud technologies across AWS, Azure, and GCP while supporting software design, deployment, documentation, and maintenance.

Develops web, desktop, and cloud applications using Angular, React, .NET Core, Node.js, and Python. Designs user interfaces, performs testing and code reviews, translates requirements into software designs, and collaborates with multidisciplinary global R&D teams. Evaluates and implements cloud technologies across AWS, Azure, and other platforms while contributing to software architecture, documentation, and feature development.

Lead the architecture, development, deployment, and maintenance of scalable Java backend services and APIs for AI-powered platforms. Integrate machine learning models into production through APIs and data pipelines, enforce testing and CI/CD best practices, conduct code reviews, and guide technical delivery. Collaborate with product, data science, and engineering teams while evaluating emerging backend, AI, and machine learning technologies.

Develop and maintain scalable backend solutions in Python, collaborate with cross-functional teams, write reusable code, manage and optimize databases, debug and profile applications, and apply best practices for performance and software design.

Lead the architecture, automation, operation, and scaling of database platforms across AWS and on-premises environments. Build infrastructure-as-code, self-service tooling, schema migration workflows, compliance controls, and reference implementations. Establish technical standards, mentor engineers, collaborate across software, data, and SRE teams, support audits, and participate in a 24/7 on-call rotation.

Build and maintain production-grade automation solutions for global accounting teams using Python, n8n, UiPath, SQL, APIs, and AI agents. Develop integrations, data pipelines, reconciliations, validations, exception handling, testing, logging, access controls, and audit trails. Partner directly with accounting stakeholders to translate business problems into deployed solutions and own them through ongoing improvement. The role also involves prototyping with LLMs, agent workflows, and AI-assisted development tools.

On-site Python Developer in Gurugram responsible for designing, developing, and maintaining backend systems. Implement OOP principles, optimize PostgreSQL databases, debug, collaborate with cross-functional teams, and deliver scalable, high-quality software using frameworks like Django or Flask and version control (Git).

Develop and maintain Python and React tools supporting model portfolio data workflows and platform enablement. Translate business requirements into production-ready solutions, automate processes, manage data distribution, support releases, conduct UAT, monitor infrastructure resilience, and resolve production issues. Collaborate with investment, infrastructure, and technology teams while maintaining runbooks and governance controls across the models ecosystem.

Design, develop, integrate, and support Salesforce-based marketing technology and business systems. Own lead lifecycle, campaign operations, account and territory management, partner portals, Outreach workflows, data quality, APIs, and inbound/outbound integrations. Translate requirements into scalable solutions, perform testing and deployment, maintain documentation and monitoring, improve platform governance, and collaborate with Marketing Operations, Sales, Revenue Operations, Data, and Engineering teams.

Lead QA strategy, team management, automation framework development, CI/CD quality gates, and AI-enabled testing initiatives. Develop and maintain UI, API, integration, regression, and end-to-end automation; conduct code reviews; monitor quality metrics; investigate defects; and partner with engineering, product, DevOps, and business teams to improve release quality.

Own quality for AI-powered platform applications by designing functional and non-functional test strategies, automating APIs with Java and RestAssured, validating data across APIs and databases, debugging services and infrastructure, conducting JMeter performance tests, maintaining CI/CD pipelines, monitoring production health, and managing defects. The role also improves agentic AI workflows for test planning, automation, execution, triage, reporting, and production validation.

Designs, deploys, and manages large-scale HPC and AI infrastructure across on-premises, cloud, and hybrid environments. The role covers XPU clusters, GPUaaS, AIaaS, Kubernetes, Slurm, networking, storage, automation, monitoring, performance optimization, security integration, and operational documentation. Provides technical guidance for high-performance workloads and distributed AI model execution while optimizing scalability, reliability, energy use, and cost.

Designs, deploys, and manages large-scale HPC and AI infrastructure across on-premises, cloud, and hybrid environments. Responsibilities include architecting GPU/XPU clusters, administering Linux and Slurm/Kubernetes platforms, optimizing performance and cost, integrating AI workloads and data pipelines, monitoring resiliency, automating infrastructure with Terraform and Ansible, and supporting users running advanced models and simulations.