Top Python Developer Jobs in Delhi

Investigate fraud and insider-threat cases from initial signal through resolution. Gather and analyze logs, transactions, behavioral signals, and other evidence; triage alerts; reconstruct events; document findings objectively; maintain evidence chain of custody; and collaborate with Detection & Response, Legal, and HR. The role also contributes to investigation playbooks, detection improvements, and fraud and insider-risk prevention.

Design and execute end-to-end automated test strategies for service portals across IP/MPLS networks. Develop Robot Framework test scripts, validate orchestration and automation, troubleshoot issues, integrate tests into CI/CD, mentor junior QA engineers, and report quality and performance metrics to ensure reliable product releases.

Develop, fine-tune, deploy, and optimize AI/ML models and LLM-based RAG solutions using Python and AWS Bedrock. Build REST APIs and microservices with FastAPI or Flask, deploy scalable cloud infrastructure, manage containerized workloads, and develop data pipelines and ETL processes. Work with relational and NoSQL databases, CI/CD, and AWS services while collaborating with technical and product teams and documenting AI workflows.

Lead customer-facing technical implementations for Fortune 500 clients. Discover and optimize third-party risk and compliance processes, configure workflows, forms, logic, scoring, approvals, integrations, and data flows in Certa Studio, and debug cross-system issues. Own project governance, testing, cutover, go-live, hypercare, and handoff. Use production LLMs and scripting to build solutions, identify platform limitations, and create reusable templates and accelerators.

The Site Reliability Engineer will design scalable infrastructure, automated deployment pipelines, monitoring, and alerting systems. Responsibilities include troubleshooting production incidents, participating in on-call rotations, maintaining systems, improving security and compliance, optimizing reliability and efficiency, and mentoring junior engineers. The role requires collaboration with development and cross-functional teams, technical communication, infrastructure automation, and expertise in distributed systems, cloud platforms, and networking.

Design, develop, and deploy production-grade generative AI and agentic systems, including RAG pipelines, autonomous agents, prompt architectures, and enterprise integrations. Build solutions using LangChain, LangGraph, vector databases, cloud services, APIs, and DevOps tooling. Advise stakeholders, document and monitor AI applications, mitigate technical and security risks, modernize RPA assets, and mentor junior developers.

Build and improve reliable, scalable distributed content delivery systems. Define SLIs and SLOs, enhance monitoring and alerting, analyze performance data, resolve complex incidents, automate operational tasks, and participate in architecture reviews. The role requires scripting, Oracle SQL analysis, Unix/Linux expertise, and experience with observability tools including Prometheus, Grafana, ADBMS, and Datadog. Collaboration with product and cross-functional teams is central to ensuring high availability, performance, and resilience.

Leads client-facing delivery of complex data, analytics, AI, cloud, and modern data platform solutions. Owns technical workstreams from discovery through deployment, designs pipelines and integrations, modernizes legacy data environments, applies testing and governance practices, troubleshoots complex issues, and ensures production readiness. Guides engineers, reviews technical deliverables, contributes to proposals and technical discovery, mentors consultants, and develops reusable engineering patterns for the Data Practice.

Leads hands-on development and modernization of web, mobile, API, and cloud-native applications. Responsibilities include writing and reviewing production code, solving complex architectural problems, mentoring engineers, applying AI-assisted development tools across the SDLC, producing technical designs, and supporting testing, security, DevSecOps, observability, operational readiness, technical scoping, and pre-sales activities.

Build, test, debug, and deploy production-quality software for client application modernization engagements. Own features and small workstreams, create technical designs, participate in code reviews, apply testing and security practices, and use AI-assisted development tools across the software lifecycle. Collaborate with client stakeholders, present technical work, manage risks, and coach junior consultants across web, mobile, API, integration, and cloud-native projects.

Analyze sales, financial, customer, and commercial performance data to support forecasting, business planning, growth opportunity identification, and executive decision-making. Build dashboards, reporting solutions, forecasts, scenarios, and actionable recommendations. Partner with business and technology stakeholders to improve data quality, KPI governance, analytics adoption, and measurement of commercial initiatives. Apply statistical, predictive, machine learning, and experimentation techniques when they deliver measurable business value.

Design, build, and maintain scalable ETL pipelines and data products across multi-cloud, multi-region environments. Investigate and resolve technical, procedural, and operational data issues, while developing efficient solutions for complex business problems. Responsibilities include data architecture, modeling, warehousing, streaming, security, monitoring, and pipeline orchestration using modern big data, programming, database, containerization, and version control technologies.

Design and build scalable data and AI infrastructure for production LLM applications. Responsibilities include developing RAG systems, embedding and vector retrieval pipelines, AI agents, LLM evaluation and observability frameworks, and batch or streaming data platforms using Databricks, Spark, Snowflake, Delta Lake, and Airflow. The role also involves optimizing distributed workloads, establishing data governance and quality practices, and partnering with engineering and product teams to productionize AI capabilities.

Build and maintain infrastructure automation across on-premises and cloud environments. Automate access provisioning, Infrastructure as Code, virtual machine and compute provisioning, deployments, security checks, remediation, and incident response. The role requires ownership of automation initiatives from design through testing, documentation, and handover while collaborating with infrastructure, DevOps, security, and development teams.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Designs, implements, and manages global cybersecurity technologies and processes. Leads security engineering projects spanning SIEM, SOAR, detection engineering, threat hunting, vulnerability management, incident response, IAM, penetration testing, and red teaming. Builds AI-powered detection and triage systems, LLM-enabled SOAR playbooks, RAG pipelines, and CI/CD workflows for security automation. Documents procedures, trains support teams, performs forensic analysis, and reports operational effectiveness.

Own and improve cloud infrastructure, Kubernetes platforms, Infrastructure as Code, CI/CD, GitOps, observability, security, reliability, and cost efficiency. Build automation and self-service tooling, lead complex production incident investigations, improve resilience and disaster recovery, evaluate architecture, and mentor engineers. The role requires autonomous platform ownership, strong systems judgment, and thoughtful use of AI-assisted engineering tools.

Designs, operates, and optimizes cloud infrastructure and internal developer platforms. Responsibilities include Kubernetes management, infrastructure-as-code automation, CI/CD, observability, troubleshooting distributed systems, cloud networking, security controls, cost optimization, and developer tooling. The role uses Go, Python, and Bash and applies AI/ML tools to improve DevOps workflows and operational efficiency across AWS, GCP, and Azure environments.

Design and maintain batch and real-time data pipelines, data lakes, warehouses, marts, and reusable dbt models using Spark, Kafka, Maxwell, S3, Trino, BigQuery, and related technologies. Optimize distributed processing, data models, queries, storage, reliability, and costs. Establish data quality, observability, governance, automation, and CI/CD practices while partnering with Product, Engineering, Analytics, Data Science, and business stakeholders.

Develop, train, evaluate, and optimize ML solutions and data pipelines; translate business requirements into production-ready ML code; perform feature engineering, model selection, and evaluation; document work, participate in code reviews, and support testing and data issue resolution.

Leads the organization’s data science and AI roadmap, managing a hands-on team that develops statistical models, experiments, forecasting, segmentation, production AI solutions, and agentic workflows. Drives GCP-based model deployment, MLOps/LLMOps, governance, observability, ethical AI, and adoption of AI tools. Partners with business, engineering, product, BI, and analytics teams to deliver practical data products, define success metrics, and communicate technical insights to executives.

Entry-level QA trainee responsible for creating and executing test cases, performing functional, regression, smoke, sanity, and integration testing, documenting defects, retesting fixes, and validating applications across browsers and devices. The role involves collaboration with developers and analysts, test documentation, and learning API, database, and automation testing using modern QA tools.

Lead full-stack software architecture and development, with a frontend emphasis, across scalable backend services and frontend microapps. Drive technical design, reliability, DevOps, CI/CD, and cloud-native engineering; mentor engineers and collaborate across distributed teams. Build AI-powered platform features using LLMs, agents, and related frameworks. Improve React performance, service reliability, incident response, and engineering effectiveness while integrating customer-focused solutions across multiple domains.

Designs and executes functional, regression, integration, UI, and API testing; develops automation frameworks; tracks and analyzes defects; integrates tests into CI/CD pipelines; validates production issues and hotfixes; collaborates with developers on fixes; participates in design reviews; maintains QA documentation; and improves testing processes, coverage, reliability, and release confidence.

The Senior Software Engineer will work on building and operating scalable backend services, APIs, and infrastructure while collaborating closely with customer engineering teams to resolve real-world challenges in enterprise data systems.