Top Python Developer Jobs in Delhi

Develop iJTAG (IEEE 1687) based test solutions and DFT methodologies (scan, BIST, hierarchical access). Build AI/ML features for pattern compaction and failure analysis, create automation frameworks and scripts (Python/C++/Tcl/Perl/Shell), and build APIs/pipelines for large-scale ATE data ingestion (STIL/WGL) and visualization dashboards. Collaborate in fast-paced, team-oriented environment and escalate critical issues.

Lead development of iJTAG (IEEE 1687) test solutions and DFT methodologies (scan, BIST, hierarchical access). Build ML features for pattern compaction, failure prediction, and root-cause analysis. Develop scalable automation frameworks in Python/C++, streamline workflows with Tcl/Perl/Shell, and create APIs/pipelines for large-scale ATE log and pattern ingestion, processing, and visualization.

Design and implement high-performance Go services for large-scale edge appliances. Build reliable content distribution, synchronization, and fault-tolerant systems across a 10K+ fleet. Optimize disk I/O, storage, and large-file transfers; integrate with AWS (S3, EC2, CloudFront, CloudWatch). Develop automation, observability, and CI/CD tooling; debug production distributed environments and ensure remote operability and resilience under intermittent connectivity and hardware variability.

As a Senior Engineer for Strategic Data Solutions, you will manage data warehousing, reporting, and analytics for internal operations and external partners. Responsibilities include collaborating with business units, providing insights, and ensuring high-quality data reporting.

The role involves utilizing your coding skills to guide team members and develop solutions for client challenges, enhancing overall success.

The Associate Staff Engineer will leverage their coding knowledge and collaborate with teammates to solve client challenges and enhance solutions.

The Staff Engineer, Python will leverage tech knowledge to guide teams, solve client challenges, and enhance solutions for significant impact.

The Google Cloud AI Specialist will design and implement AI solutions using Google Cloud Platform to enhance decision-making and provide actionable insights, develop machine learning models, and ensure data quality.

The Google Cloud Engineer will design and manage GCP infrastructure, implement IaC, optimize performance, and enhance security while collaborating with teams.

The Google Cloud Data Engineer will design and implement data pipelines using Google Cloud technologies and collaborate with analysts to ensure data quality and governance.

Design and maintain the GCP infrastructure and DevOps practices, implement CI/CD pipelines, optimize resources, and enhance application performance.

The role involves evaluating and modernizing existing applications using Google Cloud technologies, guiding clients through migration, and implementing cloud-native architectures while optimizing performance and security.

The Big Data Engineer will design and develop scalable data solutions, build data pipelines, ensure data quality, and collaborate across teams.

Design and implement test plans, write test code, validate software/SDKs and AI models for geospatial AI products. Troubleshoot issues, collaborate with data scientists, create documentation, samples, tutorials, and develop pretrained geospatial AI models for imagery, 3D, and text.

Design, build, and deploy production-grade LLM, RAG, and agentic AI systems. Implement document intelligence pipelines, use LangChain/LangGraph/LlamaCloud, manage embeddings and vector DBs, and scale services on AWS/GCP/Azure with strong ownership.

Operate and administer production Enterprise Linux infrastructure and middleware to ensure availability, security, performance, and observability. Deploy and maintain middleware (proxies, messaging), automate configuration (Ansible), provide L3 end-to-end troubleshooting across OS/network/middleware, lead incident response and RCA, collaborate with development teams on reliability and modernization, and produce runbooks and automation to reduce outages.

Lead and execute data and permission migration projects from on-premises and cloud sources to Egnyte. Liaise with customers and internal teams to monitor, report, troubleshoot, and escalate migration jobs. Drive technical discussions, provide implementation support, and ensure successful deployment and adoption while following SOPs and maintaining customer communication.

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.

The Senior Machine Learning Engineer will design, develop, and deploy AI systems, focusing on Generative AI and LLM integration across various business operations, ensuring high performance and scalability.

Develop and maintain back-end web applications, ensuring efficient integration with front-end systems, optimizing performance, and collaborating with QA for testing.

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.