Top Python Developer Jobs in Delhi

Design and lead enterprise-scale data and AI architectures centered on Snowflake and Microsoft Azure. Responsibilities include data platform modernization, cloud migration, data governance, Snowflake optimization, Python and SQL development, and GenAI solutions using LLMs and RAG. The role leads technical teams, conducts architecture reviews, mentors engineers, translates business requirements into solutions, creates roadmaps and governance documentation, and ensures security, compliance, performance, and cost optimization.

Designs and maintains scalable data engineering solutions using Python, SQL, and Snowflake. Builds ETL/ELT pipelines, data warehouses, data lakes, and React applications while integrating generative AI, LLMs, and RAG architectures. Implements Snowflake data architectures, CI/CD, testing, monitoring, and governance practices. Collaborates with stakeholders and cross-functional teams to deliver data-driven solutions, analyze business data, and provide technical leadership and mentorship.

Leads applied geospatial research supporting climate resilience, clean air, sustainable mobility, and urban planning. Develops reproducible Google Earth Engine and Python workflows, processes satellite and socioeconomic data, applies statistical and machine-learning models, validates results, and creates datasets, maps, dashboards, reports, and policy tools. Collaborates with government and research partners, supports stakeholder training, maintains documentation and data pipelines, and coordinates analytical workstreams across programmes.

Develop, enhance, maintain, and support Tableau dashboards and reports. Responsibilities include creating calculated fields and LOD expressions, connecting data sources, writing SQL, troubleshooting refresh and performance issues, validating reporting accuracy, monitoring scheduled refreshes, documenting solutions, and collaborating with business users and BI teams. The role also supports Tableau Server/Cloud administration and user access management.

Build and evolve secure, scalable AI-enabled enterprise applications and business solutions. Translate ambiguous stakeholder needs into production-ready software, automation, integrations, and architectures. Use agentic coding and AI-assisted development practices, collaborate with cloud, data, and integration teams, and ensure compliance with enterprise standards. The role requires full-stack development, cloud-native engineering, stakeholder partnership, and the creation of reusable engineering patterns for future AI-enabled solutions.

Conducts end-to-end research, including problem formulation, study and sampling design, instrument development, fieldwork, data management, quantitative and qualitative analysis, and research governance. Uses R, Python, SPSS, or Stata for microdata analysis and applies machine learning techniques. Develops reports, presentations, policy briefs, and academic publications while engaging stakeholders and ensuring data quality, ethics, and privacy compliance.

The Senior Data Analyst will build and operate the data backbone for Operations by consolidating, cleaning, analyzing, and visualizing data across units and schemes. The role produces reliable dashboards and reports, identifies risks, anomalies, and performance gaps, validates data with Operations teams, and ensures reporting reflects actual conditions. Hands-on expertise in Python, MySQL, SQL, and Power BI is required, along with analytical independence and sound judgment.

Develop, maintain, and expand automated frontend and backend test suites for the Pipes advertising QA platform. Build end-to-end tests, integrate testing into CI/CD pipelines, monitor coverage and test flakiness, diagnose failures, and improve testing frameworks and processes. Collaborate with developers, product managers, and engineering leads across three agile squads while documenting defects and contributing to testing strategy and best practices.

Senior Data & AI consultant responsible for designing, developing, and delivering cloud data and AI solutions on Microsoft platforms. Lead data engineering, platform modernization, ML readiness, MLOps, and Azure-based deployments while collaborating with customers and teams to drive adoption and technical excellence.

Develops and maintains automated frontend and backend test suites for the Pipes advertising QA platform. Responsibilities include E2E testing, test coverage monitoring, CI/CD pipeline execution, diagnosing flaky tests, improving testability, documenting defects, and collaborating with developers and product managers across three agile engineering squads.

Build foundational AI and machine learning capabilities for Twilio’s Conversation Memory team. Responsibilities include researching LLM orchestration, retrieval, personalization, and autonomous agents; designing rapid experiments; and developing scalable, production-grade ML systems using cloud services, high-volume data, streaming, and real-time inference. The role also requires technical leadership through mentorship, design reviews, and engineering standards without formal management authority.

Build production-grade machine learning systems for Twilio’s Customer Memory Team. Responsibilities include researching LLM orchestration, retrieval, personalization, autonomous agents, and statistical ML; designing experiments; deploying scalable cloud services; and integrating AI development stacks. The role requires end-to-end ownership of ambitious ML systems, technical leadership through mentorship and design reviews, and collaboration in an ambiguous, fast-paced environment.

Build foundational AI and machine learning capabilities for Twilio’s Customer Memory platform. Responsibilities include researching LLM orchestration, retrieval, personalization, and autonomous agents; designing rigorous experiments; and deploying production-grade ML systems at scale. The role requires end-to-end ownership, cloud-based services, high-volume or real-time inference, technical leadership, mentorship, and collaboration across globally distributed teams.

Architects and builds production LLM applications, including copilots, chatbots, AI agents, RAG systems, and agentic workflows. Owns AI backend services, APIs, inference orchestration, LLMOps, monitoring, CI/CD, and system optimization for quality, cost, latency, and reliability. Partners with product, platform, and security teams, promotes responsible AI and security-by-design, and mentors engineers. Requires strong Python and backend engineering expertise, distributed systems knowledge, cloud and containerization experience, and hands-on generative AI delivery.

Lead and scale data engineering teams, mentor engineers, define roadmaps, and own the architecture, delivery, and operation of data and financial reporting platforms. The role requires hands-on technical leadership across data pipelines, warehouses, event-driven systems, APIs, microservices, testing, and system design. The manager will collaborate with product and leadership, support recruiting, make architectural decisions, and participate in incident response and on-call rotations.

Lead environmental data management initiatives across global projects: oversee EQuIS/Locus systems, integrate and QA/QC laboratory and field data, design dashboards and visualizations, perform SQL development and statistical analysis, manage data migrations, coordinate with labs and clients, mentor junior staff, and develop SOPs and automated workflows to ensure compliant, decision-ready environmental data solutions.

Develop and support enterprise AI and software solutions end to end, from technology selection and architecture through development, deployment, infrastructure management, CI/CD, database administration, identity integration, and production support. Build LLM, RAG, and agentic AI applications; establish engineering standards and documentation; troubleshoot across application and infrastructure layers; and enable collaborative development at scale.

Develops and maintains business intelligence reports, dashboards, data models, and visualizations using Power BI, DAX, SQL, and enterprise data platforms. Extracts, transforms, validates, and analyzes data to generate actionable insights and support decision-making. Collaborates with stakeholders to gather reporting requirements, communicates findings, supports data governance, and improves reporting processes through automation and emerging technologies.

Provides global technical support for AlgoSec customers and partners, troubleshooting cloud infrastructure, Linux systems, applications, networking, security environments, and enterprise software. Investigates complex issues, coordinates with Support, R&D, Delivery, Product, and Sales teams, manages cases through resolution, and develops scripts and automations to improve support efficiency and reliability. Maintains technical documentation and contributes to support process improvement while working flexible hours in a 24x7 organization.

Build and deploy production AI automations across accounting, compliance, sales, marketing, HR, and finance. The role covers discovery, coding, deployment, monitoring, debugging, support, ROI measurement, and business handover. The engineer will develop LLM-based systems, integrations, agents, and workflows using Python, APIs, databases, warehouses, and business tools. They will partner with non-technical stakeholders, independently own systems end to end, train business users, maintain infrastructure, and identify high-value automation opportunities.

Leads DevOps engineering as an individual contributor, developing multi-stage CI/CD pipelines, managing source control and repositories, containerizing applications with Docker, operating production Azure Kubernetes Service clusters, provisioning Azure infrastructure through infrastructure-as-code, and automating operational tasks with Python, shell, or PowerShell scripting.

Lead DevOps engineering as an individual contributor, designing multi-stage CI/CD pipelines, managing source-control repositories, and deploying containerized applications. Maintain and troubleshoot production Azure Kubernetes Service clusters, provision Azure infrastructure through infrastructure-as-code tools, and automate operational tasks using Python, shell, or PowerShell scripting. Apply current AI tools to DevOps workflows.

Develop and support enterprise software, APIs, workflow automations, AI agents, data integrations, and cloud solutions. Build production-ready applications using .NET, JavaScript frameworks, Python, Azure, LLMs, retrieval systems, and agent frameworks. Translate business needs into secure technical solutions while implementing responsible AI, privacy, cybersecurity, governance, testing, CI/CD, observability, and infrastructure automation. Collaborate with Legal, technology, and business stakeholders in Agile teams to deliver and continuously improve transformation initiatives.

Develop and support enterprise software, APIs, integrations, workflow automations, cloud solutions, and AI-agent applications. Translate business needs into secure, scalable products while implementing responsible AI, privacy, cybersecurity, data governance, and audit controls. Build LLM solutions using Python, retrieval-augmented generation, vector databases, tool calling, and multi-agent frameworks. Contribute to Azure cloud engineering, DevOps, CI/CD, infrastructure automation, Agile delivery, documentation, and cross-functional stakeholder collaboration.

Designs, develops, tests, deploys, and maintains scalable software applications, APIs, and microservices. Responsibilities include requirements analysis, technical design, coding, debugging, code reviews, automated testing, production support, and collaboration with product, architecture, QA, and engineering teams. The role follows Agile practices and contributes to secure, maintainable software and operational excellence.