Top Python Developer Jobs in Delhi

Lead SOC incident response, threat hunting, and digital forensics using SIEM/EDR/NDR and case management tools. Mentor junior analysts, coordinate client communications during incidents, tune monitoring systems, and document actions for audit readiness.

Lead end-to-end QA for Fiat/BigPay: plan tests, design and execute cases across App/Web/Backend/Admin, maintain Python pytest+k6 automation framework, drive feature and edge-case testing, and collaborate cross-functionally to close quality loops.

Lead development of end-to-end NLP and Generative AI solutions including text classification, summarization, RAG systems, conversational AI, and document intelligence. Build and fine-tune transformer-based LLMs, design embeddings and semantic search, deploy models with MLOps (Azure preferred), monitor performance, and collaborate with product and engineering teams.

Lead a data science team to design, build, and deploy ML models and scalable PySpark data pipelines on Azure. Oversee feature engineering, model lifecycle, monitoring, performance tuning, MLOps/CI-CD, and translate business needs into analytical solutions while presenting results to stakeholders.

Lead customer-facing development and deployment of production-ready ML, Generative AI, and agentic AI solutions for supply chain challenges. Partner with customers from discovery through adoption, build scalable ML pipelines and prototypes, evaluate emerging AI technologies, collaborate cross-functionally, and mentor team members to drive measurable business impact.

Design, build, and maintain scalable ETL/ELT and real-time data pipelines on AWS. Manage Airflow DAGs, Kafka ingestion, Redshift optimization, data quality checks, API integrations, and cost optimization while collaborating with BI for reporting needs.

Develop RTL for digital blocks, perform lint/CDC analysis and DFT support, assist in writing functional tests and simple UVM components, collaborate on verification and coverage closure, support AMS co-simulation and mixed-signal integration, and work with physical design on synthesis and timing.

Build the AI layer for SigNoz: implement agent orchestration, long-running execution, streaming APIs (SSE), and MCP server integrations to expose observability data to LLM agents. Ship production LLM features, design scalable distributed backends in Go and async Python, and drive projects end-to-end in a remote-first environment.

Lead customer-facing data engineering engagements: design and implement scalable batch and real-time data pipelines and platforms (Databricks, Delta Lake, ClickHouse, Azure). Own solution delivery, optimize performance and costs, build data products for analytics and AI, drive governance and reliability, collaborate across product and customer success, and mentor junior engineers.

As a Software Engineer, you'll build tools, improve APIs, manage QA infrastructure, grasp business needs, and ensure system scalability.

Early-career Product Manager in analytics/strategy focusing on behavioral data analysis, A/B testing, funnel diagnostics, and recommendation ranking optimization. Produce strategy docs and PRDs, collaborate with algorithm teams, develop labels/taxonomies, and deliver actionable insights from experiments and data extraction.

Develop, debug, and maintain Python 3 code for serverless AWS services (Lambda, S3, SQS, SAM). Build HTTP APIs and event-driven systems, work with relational databases, use CI/CD, Git/GitHub, Jira/Confluence, and apply TDD/BDD while collaborating with remote teams across time zones.

Design, develop, and maintain backend software and RESTful APIs using Java and Spring Boot. Write and run TestNG unit tests, implement NoSQL data solutions (MongoDB/Cassandra/Redis), use Git for version control, debug defects, collaborate in Agile teams, and improve development processes.

As a Full Stack Developer, design and maintain web applications using JavaScript frameworks, develop APIs, collaborate in Agile teams, and mentor junior staff.

Lead end-to-end design, development, deployment, and monitoring of computer vision and generative AI systems. Optimize training/inference pipelines, collaborate cross-functionally, and drive scalable production solutions for photography-related vision use cases.