Top Python Developer Jobs in Delhi

Build and deliver scalable SaaS features for government agencies using ReactJS, TypeScript, GraphQL, NodeJS, and Python. Responsibilities include end-to-end development, secure authentication and authorization, debugging, cross-functional collaboration, process improvement, and mentoring junior engineers. The role also involves AI-driven UX, workflow automation, observability, event-driven architecture, and advocating for modern engineering practices.

The Data Scientist will implement GenAI systems, build components, support project delivery, analyze findings, and deliver technical solutions, focusing on NLP and deep learning.

Leads technical direction and architecture for Twilio Flex’s high-scale TaskRouter backend services. Designs distributed systems, APIs, data architectures, and migration strategies; improves scalability, resilience, availability, observability, and operational excellence. Partners across engineering and product, influences roadmaps, leads design and code reviews, mentors engineers, and supports incident response. Requires extensive backend development experience, AWS expertise, distributed systems knowledge, and experience with databases and CI/CD.

Develop and maintain SAP ABAP solutions, customizations, reports, workflows, and integrations. Build SAP data extraction and integration pipelines using ODP, CDS Views, OData, and SAP Datasphere. Translate business requirements, especially SAP SD processes, into scalable technical solutions and integrate SAP data with cloud platforms. Collaborate with functional consultants, data engineers, stakeholders, and external partners while independently resolving technical issues and strengthening internal SAP capabilities.

Designs end-to-end technology solutions for media, OTT, and digital platforms. Translates business requirements into scalable and secure architectures, evaluates technologies, recommends integrations, and guides implementation. Collaborates with business, engineering, infrastructure, and technology teams while ensuring alignment with enterprise architecture and security standards. The role requires AWS expertise, cloud and microservices experience, infrastructure as code, programming and scripting skills, solution documentation, and stakeholder management.

Build and operate production AI systems for financial products, including LLM pipelines, RAG, document extraction, anomaly detection, model serving, and automated reconciliation. Develop reliable backend integrations, vector search infrastructure, monitoring, evaluation, observability, and human-in-the-loop workflows. Collaborate with product, backend engineering, and data science teams to establish scalable AI engineering practices.

Build and deploy production-ready Generative AI and LLM applications, including RAG systems, chatbots, copilots, intelligent automation, and agentic workflows. Develop Python backend services, integrate LLM APIs, optimize model quality and performance, implement evaluation and guardrails, and troubleshoot deployed systems. Collaborate with engineering, product, operations, and business teams in a globally distributed environment while translating AI capabilities into scalable production solutions.

Provides technical support for a zero-trust cloud networking platform. Resolves, escalates, tracks, and closes customer incidents and service requests within SLAs. Troubleshoots WAN/LAN, software-defined networking, cloud networking, firewalls, ACLs, and cloud security environments across AWS, Azure, and GCP. Works rotational 24/7 shifts with globally distributed teams and supports incident, change, and problem management processes.

Supports homeowners insurance pricing through dataset preparation, data validation, competitive rate analysis, rate filing exhibits, GLM pricing model development, and performance reporting. The role assists with actuarial analyses under senior guidance, participates in technical reviews, supports actuarial tools, and progresses toward professional actuarial credentials.

Analyze courier supply and dispatch operations across onboarding, retention, availability, earnings, assignment, acceptance, and delivery success. Build reliable metrics, dashboards, and self-service reporting; identify root causes of performance changes; evaluate experiments, incentives, and product initiatives; validate data quality; and translate findings into recommendations for product, operations, and leadership.

Own platform architecture, infrastructure security, access controls, database maturity, and AWS migration. Write secure production code, establish infrastructure standards, review security changes, partner with InfoSec, mentor engineers, troubleshoot recurring failures, and guide DevOps implementation. The role also supports AI infrastructure, model serving, agent observability, React and TypeScript applications, and infrastructure security on-call responsibilities.

Design, develop, deploy, and optimize production-grade Generative AI and LLM-powered systems. Build agentic pipelines with orchestration, tool integration, feedback loops, asynchronous processing, and event-driven architectures. Collaborate across operations, marketing, customer experience, and product teams to identify AI opportunities. Troubleshoot systems, maintain documentation, communicate technical findings, and deliver scalable solutions using Python, cloud platforms, modern AI frameworks, and containerization.

Build, test, deploy, and maintain production automations using n8n, custom code, AI models, PostgreSQL pipelines, and cloud platforms. Own automation initiatives end to end, integrate systems, monitor reliability and business outcomes, maintain CI/CD pipelines, respond to incidents, troubleshoot failures, and support security and compliance reviews. The role also involves stakeholder collaboration, process improvement, research into emerging automation technologies, and on-call participation.

Provides database consulting and reliability services for customer environments, including MySQL database design, administration, monitoring, backup and disaster recovery, replication, failover, troubleshooting, and infrastructure optimization. Advises clients on operational best practices, supports incident and change management, enhances automation and monitoring tools, contributes to presales and technical content, and mentors database consultants.

Graduate software engineers will develop open-source and commercial software across the Linux stack, from firmware, kernels, and drivers to desktop applications, containers, clouds, and Kubernetes. Responsibilities include writing resilient, performant code, contributing to product roadmaps, engaging with users and open-source communities, addressing customer issues, and developing secure software. The role is globally remote with twice-yearly international team sprints and emphasizes mentorship, collaboration, and continuous technical growth.

Lead client advisory, solution design, delivery, and productization of AI-powered workflows and automation. Build GenAI solutions, integrations, document-processing systems, and secure knowledge assistants across business platforms. Develop reusable accelerators and service offerings, monitor production performance, improve accuracy and cost, and identify new client opportunities. The role requires hands-on experience with LLMs, RAG, agent frameworks, APIs, Python or TypeScript, business systems, AI security, and production deployment.

Design, develop, and deploy autonomous AI agents and LLM-powered systems for resume optimization, job matching, career coaching, and AI interviews. Build multi-agent workflows, RAG systems, vector databases, prompt engineering and fine-tuning pipelines, agent evaluations, and human-in-the-loop oversight. Integrate LLMs into web applications and browser extensions while ensuring production scalability and reliability. Collaborate with engineering, product, design, and company leadership, and develop traditional machine learning workflows where needed.

As a Senior QA Engineer, you will design test strategies and automation for optical networking solutions, ensuring quality and performance through manual and automated testing, and collaborating with engineering teams.

Support and troubleshoot production GCP data pipelines across batch and streaming workloads. Diagnose failures, latency, performance, and configuration issues; optimize pipelines for reliability and faster refreshes; support platform upgrades and compliance; and collaborate with Data Engineering, BI, and application teams. The role requires hands-on GCP, SQL, Python, BigQuery, Spanner, Dataflow, Pub/Sub, IAM, monitoring, logging, and production support experience.

Design, build, and maintain centralized data pipelines, warehouses, lakes and hubs. Assemble complex datasets, implement ETL processes, build analytics tools and reports, support cross-functional teams, ensure secure cross-region data separation, and create product data tools to enable insights on acquisition and operational metrics.

Owns quality engineering for carrier-grade optical networking products. Responsibilities include creating test plans, validating system features and customer scenarios, developing manual and automated test scripts, maintaining test frameworks, analyzing failures and defects, and reporting quality assessments. The role requires testing SONET, SDH, OTN, DWDM, ROADM, optical amplifiers, FCAPS, provisioning, configuration, and protection schemes using test equipment and scripting languages.

Lead research and engineering work on LLM-based architectures for dialog, search, and destination entry. Optimize LLMs via prompting and fine-tuning, integrate them with ASR, TTS, and vision systems, and collaborate cross-functionally to deliver performant, resource-constrained solutions for automotive voice assistants.

Develop, optimize, and maintain scalable ETL/ELT data pipelines for structured and unstructured data using Python and PySpark. Ensure data quality through validation, cleansing, transformation, and reconciliation. Collaborate with technical and business stakeholders, troubleshoot performance issues, conduct code reviews, and implement coding standards and CI/CD pipelines. Work with big data frameworks, cloud platforms, databases, data models, schemas, and warehousing solutions.

Build scalable data pipelines for large datasets using Microsoft Fabric, Azure data services, advanced SQL, and Python/Spark. Develop data transformations and apply data modeling principles such as star schemas. Experience working with financial services data is desirable.

Provides post-sales technical support for APIs, SDKs, and developer integrations. Diagnoses and reproduces customer and partner issues, analyzes logs and payloads, distinguishes implementation issues from defects, and escalates validated problems to Engineering. Supports authentication, licensing, configuration, and troubleshooting while contributing to API documentation and knowledge resources. Collaborates with Engineering, Product, and partners on recurring issues, incident response, and improving developer enablement.