Top Python Developer Jobs in Delhi

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.

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.

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.

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.

Leads hands-on security, automation, compliance, and operational foundations for a Managed AI cloud platform. Designs CI/CD pipelines, Infrastructure as Code, security controls, compliance validation, observability, deployment automation, secrets management, AI governance, and incident response across Azure and GCP. Provides technical leadership, mentors engineers, and participates in architecture, code review, and platform governance activities.

Designs and maintains automated API and middleware test suites for gRPC-Web, Proto3, HTTP/2, JSON-RPC, and REST services. Inspects browser-generated network traffic using proxy tools and DevTools, reconstructs schemas and request behavior, produces sanitized HAR evidence, and develops controlled API replay scripts. Tests serialization, encoding, authentication, headers, routing identifiers, and malformed requests while collaborating with backend engineers on contracts and edge cases.

Lead design and development of the H1 Knowledge Graph and Search platform. Own feature lifecycle, evolve search/graph systems, optimize performance and scalability, maintain data pipelines, ensure data quality and security, mentor engineers, and drive architecture and integration patterns.

Leads global IT operations and end-user services across US and India teams. Owns IT platforms, identity and access management, networks, office infrastructure, asset management, service quality, SLAs, incident and change management, compliance, budgets, and managed service providers. Develops distributed teams, drives automation, supports site buildouts, and partners with Security and Systems Engineering to maintain reliable, secure enterprise technology.

Lead oncology patient-journey, line-of-therapy, brand, HEOR, and healthcare resource utilization analytics using structured claims and patient data. Build SQL pipelines, perform exploratory and statistical analyses, ensure data governance and reproducibility, and translate findings into dashboards and client-facing insights. Collaborate with HEOR, market access, strategy, and engineering teams while independently managing project delivery and stakeholder communication.

Lead deployment, maintenance, optimization, and reliability of production applications. Architect automation, monitoring, logging, alerting, and infrastructure improvements; identify AI-driven DevOps opportunities; mentor engineers; evaluate emerging tools; and collaborate with R&D, Support, and Security teams to resolve complex infrastructure and operational issues.

Supports enterprise sales cycles across APJ through technical discovery, solution design, demonstrations, proofs of concept, architecture reviews, tenders, and technical validation. Enables partners through training, workshops, reusable technical content, and joint customer engagements. Designs solutions involving integrations, APIs, automation, and deployment architectures while collaborating with sales, product, engineering, services, and support teams. Requires strong IT, cybersecurity, cloud, networking, scripting, communication, and partner-management expertise.

Lead oncology analytics projects involving patient journeys, line-of-therapy mapping, claims data, brand analytics, and HEOR/HCRU studies. Build SQL pipelines and use Python and Excel to analyze structured healthcare datasets, ensure data integrity, and deliver reproducible insights. Develop dashboards and client presentations while collaborating with HEOR, market access, strategy, and engineering teams. Independently manage project delivery and stakeholder communication.

Designs, deploys, administers, and optimizes secure AWS infrastructure across cloud and on-premises environments. Manages Linux and Windows servers, networking, identity services, VMware, infrastructure as code, containers, CI/CD pipelines, monitoring, security hardening, compliance, backups, and disaster recovery. Automates operational processes with Terraform, CloudFormation, Ansible, PowerShell, Bash, and Python; participates in incident response, documentation, migrations, and infrastructure planning.

Design, prototype, deploy, and maintain AI-powered internal tools and automation for business teams. Build full-stack applications, agents, workflows, enterprise integrations, and reusable components using generative AI and cloud technologies. Ensure security, governance, testing, observability, documentation, and measurable business impact while supporting adoption across Sales, Support, Product, Operations, and other teams.

The Design Verification Engineer ensures functional correctness of IPs and SoCs by developing verification environments, driving coverage closure, and automating tasks to enhance productivity.

The CAD/Flow Methodology Development leader will define and scale EDA infrastructure, automate design flows, debug issues, and establish metrics for silicon delivery efficiency in cross-functional environments.

Develop scalable, high-performance backend applications using Python. Responsibilities include Core Python and object-oriented programming, functional programming, multithreading, multiprocessing, asynchronous development, REST API creation and integration, SQL query optimization, Apache Spark distributed processing, debugging, performance tuning, and problem-solving.

Leads a cross-functional team of backend, mobile, and web engineers building global in-person acquiring products. Owns technical direction, architecture, delivery, reliability, and scaling of ePOS, Tap to Pay, mobile applications, pricing engines, payment networks, and third-party integrations. Partners with Product Management, drives platform transformation, mentors engineers, and oversees the full product lifecycle from development and testing through deployment, monitoring, and production operations.

Build and deploy production machine learning systems, including recommendation engines, semantic search, RAG pipelines, and LLM applications. Architect scalable APIs, workflows, background jobs, and data pipelines while optimizing reliability and performance. Collaborate with product and engineering teams, write tested modular code, conduct code reviews, and stay current with AI/ML and cloud-native technologies.

Design, modernize, automate, and maintain ScienceLogic’s hybrid cloud monitoring platform. Responsibilities include developing system update software, leading OS migrations and technology upgrades, troubleshooting distributed systems, optimizing performance and reliability, and contributing to Agile Scrum improvements. The role requires Linux administration, system and network troubleshooting, automation, Python and Bash scripting, SQL database knowledge, and familiarity with AI-driven software development and monitoring tools.

Owns company-wide revenue retention and add-on monetization analytics across CPaaS, AI features, Customer Success, and Finance. Builds canonical GRR, NRR, churn, and monetization metrics; applies causal inference methods to distinguish true retention drivers from bias and data artifacts; guides experimentation and forecasting; and influences cross-functional product, pricing, lifecycle, and customer success decisions without direct authority.

Owns customer growth analytics across onboarding, activation, trial-to-paid, multi-product adoption, retention, and expansion. The role develops canonical metrics, segment definitions, value models, and causal-inference standards; identifies behaviors that predict expansion; evaluates experiments; and translates product, behavioral, and revenue data into guidance for Growth, GTM, Finance, and product leaders. It also shapes event taxonomy, influences roadmap and investment decisions, and establishes reusable analytical practices across teams.

Own analytics and measurement for HighLevel’s AI product portfolio. Define instrumentation standards, feature-level KPIs, adoption and quality metrics, and rigorous approaches for evaluating nondeterministic AI features. Partner with experimentation leaders, PMs, engineering, and Analytics Engineering to close data gaps, influence product roadmaps, establish canonical metrics, and mentor future analysts.

Build and improve GoGuardian’s Pear Assessment platform across the full stack. Responsibilities include developing TypeScript and Node.js backend services, React interfaces, OpenSearch semantic search, scalable data pipelines, AWS cloud-native systems, MongoDB data models, and real-time classroom integrations. The role involves optimizing performance, reliability, and analytics, integrating AI services, collaborating cross-functionally, participating in architecture and code reviews, and maintaining strong testing and engineering standards.