Top Python Developer Jobs in Delhi

Design, build, and maintain scalable backend services and APIs; optimize performance and data pipelines; architect and manage AWS infrastructure; document systems; ensure code quality through reviews; deploy containerized microservices and implement infrastructure-as-code and monitoring/alerting for production systems.

Lead architecture and delivery of production-grade GenAI full‑stack solutions using Python and React. Design scalable LLM inference, RAG and agent pipelines, integrate cloud/on‑prem deployments, enforce security/governance, drive CI/CD and QA, mentor teams, and translate business needs into technical strategy and reusable AI platforms.

Lead architecture and delivery of production-grade Generative AI applications. Define technical strategy, design scalable LLM inference and RAG platforms, frontend AI UX, and reusable GenAI components. Ensure security, privacy, governance, and enterprise readiness while partnering with product and engineering teams and conducting POCs and technical reviews.

Own and operate the AI-native platform runtime including Kubernetes (GKE), Terraform, GCP, CI/CD, observability, orchestration (Temporal, Kong). Improve reliability, performance, cost, multi-tenant provisioning, developer experience, and lead on-call and incident response.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Own and operate the production infrastructure and delivery platform for AI products. Implement infrastructure-as-code, run Kubernetes, manage CI/CD, ensure security, reliability, observability, and compliance (single-tenant, SOC 2). Build automation and guardrails to enable safe, fast shipping.

Lead design, build, and operation of AI-powered product features across backend, frontend, and AI systems. Make architecture decisions, drive production deployments, improve code quality and reliability, mentor engineers, and own features end-to-end.

Own internal IT systems: identity/SSO provisioning, device lifecycle and MDM/endpoint security, SaaS access and licensing, onboarding/offboarding, automation of IT workflows, internal networking, and support while contributing to security and compliance.

Build and productionize AI features across frontend and backend: document review, entity extraction, quote comparison, embeddings and LLM integrations, prototype from customer feedback, and create scalable reusable systems for insurance workflows.

Build and operate ingestion and transformation pipelines to turn messy insurance data into clean datasets. Design bronze/silver/fact layers, model and tune Postgres and analytical stores, define KPIs and reconciliation checks, orchestrate observable data jobs, and ensure end-to-end data quality for analytics and AI products.

Build AI-powered product features across backend, data pipelines, and UI. Implement RAG, embeddings, vector search, prompt design, and tooling to convert messy insurance documents into reliable, source-cited capabilities and collaborate with product and customer teams.

Partner with strategic enterprise customers to define GenAI strategy, design architectures using ChatGPT/OpenAI API, build prototypes to production, manage stakeholder relationships, scale best practices and notebooks, and provide high-signal feedback to Product and Research teams.

Lead Compliance Data Analytics to research and analyze risk patterns (fraud, account takeover, payment fraud), deploy and iterate rules/models, partner with stakeholders to balance user experience and risk mitigation, drive business-centric analytics and strategic metrics, and build and manage a high-performing compliance analytics team.

Early-career role analyzing PB-scale on-chain, transactional and customer data. Perform data engineering, ETL, querying, build visualizations and ML models, translate insights for teams, manage reporting environment, secure confidential data, and troubleshoot reports/databases.

Build and deploy production-ready GenAI systems (LLM, RAG, agentic agents) and document intelligence pipelines. Work with LangChain/LangGraph/LlamaCloud, embeddings and vector DBs, deploy on AWS/GCP/Azure, and apply ML/MLOps and model evaluation. Deliver fast, independent end-to-end solutions as a freelance/consultant.

Early-career role building and evaluating LLM-powered agentic trading systems for crypto markets. Develop pipelines, prompt strategies, retrieval, model evaluations, and ship Python code for production features.

Design and implement complex backend services for Binance Square. Collaborate with product, DevOps, and QA to deliver high-quality microservices, promote engineering best practices, and perform code reviews.

Design and implement complex backend logic for the Strategy Engine to deliver personalized, dynamic UI experiences. Collaborate with data analysts to define and evaluate strategies, and work with Product, DevOps, and QA to deliver robust solutions. Promote engineering best practices and perform code reviews to ensure quality and consistency.

Lead reliability, scalability, and performance of cloud infrastructure. Own AWS architecture and CI/CD (GitHub Actions), automate deployments, maintain monitoring/alerting, support backend teams, troubleshoot production, and optimize cost and performance.

Design and maintain secure, scalable cloud infrastructure across major providers while owning cloud cost visibility and optimization. Implement IaC, tagging, budgeting and forecasting, build cost dashboards, drive rightsizing and savings measures, and partner with Finance to report FinOps KPIs and adopt cost-management tooling.

Design, develop, fine-tune, and productionize ML and Generative AI/LLM solutions. Build end-to-end ML pipelines (data ingestion, modeling, deployment, monitoring), implement RAG and vector DB architectures, apply prompt/context engineering and MLOps best practices, and collaborate with cross-functional teams to optimize models for performance, latency, scalability, and cost.

Design, deploy, monitor, and manage scalable Azure cloud infrastructure and applications using IaC and DevOps practices. Troubleshoot and perform root-cause analysis, implement disaster recovery and cost-management strategies, optimize performance and security, and mentor junior team members while supporting CI/CD pipelines and stakeholder requirements.

Support development of internal LLM-based tools (coding assistants, RAG pipelines, prompt optimization), improve engineering efficiency, test and document AI tools, and research emerging AI technologies.

Analyze PB-scale trading, transactional, operational and customer data; build visualizations, ML models and experiments; translate findings into actionable recommendations for trading, operations, product and executives in a fast-paced environment.

Build AI-driven developer tools: coding assistants, knowledge retrieval, workflow automation, and LLM-powered apps for code generation, understanding, and test automation. Collaborate with engineering teams to identify pain points, ship solutions, and experiment with prompt engineering, RAG, and AI agents.