Top Python Developer Jobs in Delhi

Join a research team building AI-driven quantitative trading tools. Design, test, and productionize predictive signals; diagnose model and PnL behavior; research market microstructure; build agentic research infrastructure; collaborate with traders and engineers to deploy monitored strategies.

Design, build, and deploy production AI agent workflows and LLM-powered systems. Integrate tools, APIs, databases, and internal services; improve agent quality, latency, safety, and cost. Collaborate with product and operations to translate business problems into practical AI solutions and contribute to backend services and evaluation/guardrail patterns.

Manage post-listing partner and operational requests (rebranding, token swaps, mainnet integration, airdrops), perform ROI and risk analyses, run projects through SOP stages, build risk-control systems, optimize processes, support analytics and post-mortems, and deliver initiatives on time.

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 QA automation for Futures across web, desktop, and mobile: design and execute test plans, set up test environments, write automated tests, perform security and performance testing, root-cause analysis, and improve testing processes across the SDLC.

Lead a Data Analyst team to analyze PB-scale transactional and customer data, build algorithmic systems and visualizations, implement ML/LLM solutions for compliance use cases, support business stakeholders with data requests, and translate findings into actionable recommendations.

Early-career role building AI-powered developer tools and internal LLM applications. Tasks include coding assistants, lightweight RAG pipelines, prompt optimization, testing, documentation, and researching emerging AI technologies to improve engineering productivity.

Conduct original research on generative AI and LLMs for crypto/finance, design and run rigorous experiments, build evaluation frameworks, develop post-training and test-time scaling methods, and collaborate with engineers to deploy research into production focused on agent reasoning, alignment, and robustness.

Early-career research role implementing and running experiments on LLM reasoning, post-training alignment, and agentic AI for crypto markets. Tasks include literature synthesis, model implementation (PyTorch/Hugging Face), experiment tracking (Weights & Biases), and collaborating with engineers to translate research into production.

Analyze operational data and processes, identify bottlenecks, design scalable solutions, automate workflows and reporting with Python and SQL, build dashboards and monitoring, perform root-cause analysis, document logic and collaborate with product, engineering, and ops teams to improve automation and data quality.

Build and deploy agentic finance AI solutions: design voice‑optimized prompts and conversational flows, integrate LLMs (Gemini/GPT/Claude) with RAG and enterprise data, implement voice/speech/telephony integrations, automate workflows and reporting using Python and SQL, and collaborate across product, engineering, and operations to tune and monitor production ML systems.

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 software engineer in a fixed-term accelerator contributing to backend/full-stack feature development, testing, CI/CD, and deployments. Collaborate on API and system design, integrate and deploy AI/LLM capabilities (LLM APIs, RAG, embeddings, agents), build datasets/benchmarks, monitor performance, debug issues, and produce technical documentation.

Test and investigate high-performance backend distributed systems: reproduce functional issues, design and run manual tests, build and maintain automation scripts, test APIs/databases/distributed services, identify coverage gaps, perform investigatory testing for unreleased features, and document test reports.

Build internal AI developer productivity tools (coding assistants, retrieval systems, workflow automation). Develop LLM-powered apps for code generation, understanding, and test automation. Integrate AI across the SDLC, embed with teams to remove friction, experiment with prompt engineering, RAG, and AI agents, and evaluate emerging AI technologies.

Design, develop, and maintain high-performance, highly available backend services and APIs for Big Data products using Java/Python. Manage full service lifecycle, capacity planning, profiling/benchmarking, troubleshooting, security, and system architecture. Contribute to open-source big data projects and build scalable tools for global operations.

Analyze marketing campaign performance, customer behavior, and market trends; build and maintain marketing dashboards and reports; collaborate with marketing and sales to define data needs; conduct competitor and KOL analysis; present insights via data visualization and recommend optimizations to improve marketing ROI and conversion.

Lead design and implementation of a sustainable points/economy system: define issuance rules, redemption, inflation control, metrics, anti-abuse strategies, and collaborate cross-functionally to integrate the system and ensure compliance and stable operations.

Early-career data scientist role to analyze user and content data, build metrics, design recommendation strategies (recall, ranking, cold-start, diversity), run A/B tests, evaluate experiments, explore LLM/multimodal applications, and improve data pipelines and feature logging.

Six-month early-career internship building LLM-powered agent workflows and production AI components. You'll design prompts, retrieval and tool-use logic, run benchmarks, improve quality/latency/reliability/safety, contribute to backend services/APIs, and ship working prototypes alongside full-time engineers.

Design, back-test and implement pricing, risk management, and agency trading strategies; collaborate with developers to integrate models into the trading platform; participate in product ideation, modeling, parameter tuning, and risk evaluation.

Design, build, test, and deploy low-latency, high-throughput trading systems; collaborate with quant and product teams; maintain trading infrastructure, risk and position-monitoring tools; mentor juniors; integrate AI coding agents into development workflows and support production incidents.

Lead integration testing for high-performance backend systems: design and implement Python-based automated tests, integrate scenarios into CI, enhance automation framework and reporting, investigate root causes, and collaborate with developers and product managers to validate and stamp features for production.

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.

Manage post-listing processes for tokens: identify and mitigate listing risks, perform on-chain and off-chain root cause analysis, handle partner and operational requests (rebrands, swaps, airdrops), oversee project implementation and stakeholder coordination, and optimize request management processes.