Top Python Developer Jobs in Delhi

Design, build, and maintain pipeline and artist-facing tools across web, desktop, and Unreal surfaces. Develop Python integrations with Maya, Unreal, Nuke, Perforce, and AYON; build AI-assisted features; and own tools through production. Collaborate directly with artists and supervisors, manage work autonomously, and deliver responsive, maintainable, well-tested software.

Resolves critical technical issues for high-value customers through root cause analysis, advanced network troubleshooting, bug reproduction, testing, and engineering collaboration. Supports cloud networking environments, Linux systems, automation, QA, documentation, and customer communication. The role also contributes to feature feedback, support content, and off-hours escalation coverage.

Leads enterprise Microsoft 365 transformation initiatives focused on SharePoint Online, Power Platform, Copilot, AI agents, automation, security, compliance, and governance. Architects and implements collaboration solutions, migrates legacy platforms, configures Purview and related controls, and develops AI governance strategies. Provides technical leadership, client engagement, workshops, training, and ongoing support while coordinating project risks and dependencies. Requires availability during Eastern Time business hours and deep Microsoft 365, security, programming, database, and cross-tenant migration expertise.

Lead enterprise-scale migrations to GitHub Enterprise Cloud, including repositories, users, access controls, workflows, Enterprise Managed Users, and data residency. Build governed identity and access environments, validate integrations, and document handoffs. Design and maintain scalable internal developer platforms, standardize tooling, and develop self-service infrastructure and CI/CD capabilities. Partner with client IT teams, mentor engineers, and apply cloud-native architecture, Kubernetes, automation, and Infrastructure as Code practices.

Develop and own highly scalable backend services for IoT, order, and payment systems. Responsibilities include Java and Python development, distributed systems design, database and cache optimization, event-driven architectures, production troubleshooting, observability, incident response, reliability improvements, and collaboration with Infrastructure, QA, and Product teams.

Leads enterprise architecture for scalable, multi-tenant SaaS systems and cloud-native platforms. Owns AWS architecture, distributed data pipelines, cost optimization, security, governance, and architectural standards. Drives GenAI and machine learning adoption across the software development lifecycle, builds self-service capabilities, guides engineering teams, conducts prototypes, and leads architecture reviews. Requires deep hands-on cloud, distributed systems, coding, infrastructure automation, AI/ML, and strategic technology leadership experience.

Integrate security into software delivery and infrastructure workflows across AWS and GCP. Build scanning into CI/CD pipelines, maintain security guardrails, secure containerized workloads, implement least-privilege access and secrets management, and support compliance with SC TRM and BNM RMiT. Improve monitoring, vulnerability reporting, incident readiness, and secure delivery practices while collaborating with platform and engineering teams using multiple programming languages.

Owns technical direction for backend services and high-throughput content-processing pipelines. Designs scalable production systems, evaluates AI/ML integrations, leads cross-team architecture initiatives, and drives systems from requirements through operations. Responsibilities include monitoring reliability, troubleshooting production issues, mentoring engineers, leading RFCs and architectural reviews, and raising engineering standards through technical leadership.

Build and own end-to-end evaluation frameworks for LLMs and multi-step AI agents, including benchmarks, automated and human-in-the-loop evaluators, golden datasets, regression suites, production monitoring, and quality dashboards. Partner with research, product, Trust & Safety, and legal teams to define rigorous metrics and translate evaluation results into training and deployment decisions. Mentor engineers and establish evaluation best practices.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

Design, build, and operate AWS and Kubernetes infrastructure powering production AI systems. Own CI/CD, infrastructure as code, GitOps, observability, secrets management, GPU workloads, model serving, reliability, security, scalability, and cost optimization. Troubleshoot production incidents, perform root-cause analysis, and develop automation using Python and Bash while collaborating across Engineering, ML/AI, Security, and Product teams.

Lead design and implementation of autonomy and edge-AI software for air, land, and sea platforms. Develop tactical autonomy algorithms, integrate software with hardware, deploy and field-test capabilities, analyze mission data, mentor teammates, collaborate cross-functionally, and support customers and defense programs.

Develop and integrate autonomy and edge-AI capabilities using the Hivemind SDK for unmanned platforms across air, sea, and space. Work with customers to deploy and debug integrations, provide technical training, document solutions, collaborate with product teams, and conduct on-site deployments and flight testing (~30% travel).

Develop and integrate Hivemind autonomy capabilities for unmanned platforms across air/sea/space. Collaborate with customers to enable edge-AI, debug integrations, deploy infrastructure at customer sites, provide technical training, document systems, and work with product/engineering to iterate. Travel to customer sites (~30%).

Manage SaaS Kubernetes applications in AWS, respond to alerts and incidents, deploy infrastructure technologies, triage client service tickets, troubleshoot operating systems, applications, virtualization, and containers, and improve operational automation and SLAs. The role requires cloud operations experience, AWS, Terraform, Kubernetes, Linux, automation, Java application support, performance tuning, container management, and full-stack troubleshooting.

Manage SaaS Kubernetes applications in AWS and OCI, respond to alerts and incidents, deploy infrastructure technologies, triage client-service tickets, troubleshoot operating systems, applications, virtualization, and containers, and develop automation to improve operational SLAs. The role supports large-scale Java applications and requires expertise in Terraform, Kubernetes, automation, performance tuning, IIS, Tomcat, and full-stack troubleshooting.

Leads the design and improvement of DevOps platforms, CI/CD pipelines, infrastructure automation, cloud environments, container orchestration, observability, security controls, and SRE practices. Partners with engineering teams to improve delivery speed, reliability, deployment quality, and developer self-service. Provides technical leadership, mentoring, governance, incident response, resilience engineering, and cloud cost optimization across production and non-production workloads.

Analyzes US healthcare claims and related datasets to identify data-quality issues, validate product outputs, investigate root causes, and recommend improvements. Develops SQL queries, dashboards, documentation, requirements, test scenarios, and validation results. Supports functional, regression, and user acceptance testing while collaborating with product, engineering, machine learning, engagement, and account teams.

Owns end-to-end QA coverage across firmware, cloud services, and mobile applications for IoT products. Tests device-to-cloud communication, APIs, business logic, connectivity failures, offline operation, OTA updates, and rollback scenarios. Analyzes device and cloud logs, develops test data strategies, prioritizes automation scenarios, and maintains smoke and regression testing processes. Requires systems thinking, embedded and mobile testing experience, API testing, Agile QA processes, and familiarity with relevant test management tools.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality, data discoverability, testing, feature releases, quality standards, and customer-focused insights. Uses scientific expertise, Python and Bash scripting, databases, cloud platforms, and AI-enabled testing workflows to improve data integrity and platform performance. Coordinates stakeholders, documents work, presents findings, and drives cross-functional projects.

Analyzes US healthcare claims and related datasets to identify data-quality issues, patterns, coverage gaps, and product opportunities. Develops SQL queries, dashboards, validation processes, documentation, and test scenarios; investigates defects and performs root-cause and impact analysis. Collaborates with product, engineering, machine learning, engagement, and account teams to support healthcare data-product delivery, client data services, product enhancements, and proof-of-concept initiatives.

Owns and administers Snowflake data platforms, including account governance, performance and cost optimization, security, access management, and reliability. Maintains Fivetran ingestion connectors and dbt pipelines, integrates monitoring and alerting, troubleshoots data issues, and supports cloud DevOps across AWS, Azure, and OCI. Partners with engineering, security, and compliance teams on access reviews, audit evidence, incident response, and platform improvements.

Own and evolve Mitratech’s AWS DevOps and platform engineering practice, including multi-platform CI/CD, Terraform and AWS CDK infrastructure as code, AWS compute, networking, databases, security controls, and AI/analytics operations. Design reusable pipelines, enforce quality and security gates, manage multi-account environments, improve automation reliability, mentor engineers, and participate in on-call support.

Build and maintain Five9’s engineering intelligence platform through Looker dashboards, Python and SQL data pipelines, API integrations, data quality monitoring, anomaly detection, and executive-ready analysis. Integrate Jira, Jellyfish, Salesforce, and other systems to provide engineering leadership with insights into delivery, capacity, investment, resource contention, and portfolio health. Document data lineage and analytical methodologies while optimizing models and dashboard performance.

Leads a team of scientific data analysts supporting Reaxys, Embase, and future AI-enabled life sciences platforms. Oversees search and API quality evaluation, data discoverability, testing, feature releases, customer-focused insights, and quality standards. Uses scientific expertise, data analysis, scripting, databases, cloud platforms, and AI-enabled testing workflows to improve platform reliability and relevance. Coordinates stakeholders, advances projects, documents work, and presents recommendations for product and data improvements.