Top Python Developer Jobs in Delhi

Supports Indonesian and broader Asian survey operations by monitoring data quality, managing quality-control staff, correcting survey issues, analyzing collection progress, and developing data-quality tools. Coordinates field operations, training, stakeholder engagement, translations, and backcheck processes. Provides insights on coffee and cacao sustainability data to country teams and supply-chain stakeholders. The role is fully remote from Indonesia with travel to regional agricultural areas.

Develop and maintain scalable full-stack web applications across backend, frontend, databases, APIs, and cloud platforms. Responsibilities include designing applications, building services and responsive interfaces, optimizing SQL and NoSQL databases, integrating third-party services, troubleshooting production issues, writing secure maintainable code, and participating in testing, code reviews, and deployment. The role requires analytical problem-solving, understanding business requirements, effective communication, and adaptability across technology stacks.

Architect and build production-grade generative AI and agentic AI applications, including LLM workflows, multi-agent systems, RAG pipelines, APIs, and ML/GenAI infrastructure. Develop and optimize models, manage deployment and monitoring, apply responsible AI practices, and collaborate directly with customers. The role also involves mentoring engineers and shaping AI platform strategy.

Architects and builds scalable generative and agentic AI products from prototype through production. Designs LLM workflows, prompts, multi-agent systems, RAG pipelines, and APIs using LangChain and LangGraph. Owns ML and GenAI pipelines, including training, deployment, monitoring, and lifecycle management. Works directly with customers and cross-functional teams, promotes responsible AI, mentors engineers, and shapes AI platform strategy.

Deploy and configure Kinaxis Maestro production scheduling solutions, gather and document customer requirements, perform data integration and validation, train end users, provide technical support, and assist with solution testing. The role translates supply chain requirements into software specifications, supports complex customer implementations, collaborates with agile teams, and mentors junior consultants.

Build and deploy production-ready AI agents and multi-agent systems on Azure. Design RAG pipelines, orchestration patterns, tool integrations, APIs, prompt engineering workflows, evaluation loops, and unstructured-data pipelines. Implement CI/CD, monitoring, responsible AI controls, and enterprise integrations with Microsoft Teams and M365 Copilot. The role requires hands-on GenAI/LLM engineering, Python development, cloud infrastructure expertise, and flexibility across data engineering, low-code configuration, and AI-native development tools.

Design, build, and operate reliable batch and streaming data pipelines, Airflow workflows, dbt models, and analytical data infrastructure. Own ClickHouse architecture, performance, ingestion, and scalability, while supporting BigQuery and NoSQL databases. Implement data quality, observability, monitoring, incident response, backfills, schema evolution, containerization, and CI/CD. Collaborate with analytics, data science, product, and engineering teams to deliver scalable, cost-efficient data platforms.

Leads enterprise network automation and modernization using Network-as-Code, Infrastructure-as-Code, GitOps, Terraform, Ansible, Python, and CI/CD. Designs Kubernetes networking with Calico, OVN, VXLAN, and CNIs; architects BGP, OSPF, data center, and hybrid networks; and serves as the highest-level escalation engineer. Drives root cause analysis, reliability improvements, automated security validation, and zero-downtime deployments across complex global infrastructure.

Leads the design and implementation of Salesforce Data Cloud solutions, translating business requirements into technical designs, data models, schemas, and ingestion pipelines. Integrates Data Cloud with Marketing Cloud Personalization and other MarTech platforms for audience activation. Manages development teams, ensures delivery quality, troubleshoots complex integrations, supports segmentation and journey orchestration, and drives data governance, privacy, performance, mentoring, and roadmap initiatives.

Designs and leads Salesforce Data Cloud solutions, including data models, ingestion pipelines, identity resolution, integrations, activation, segmentation, and personalization. Translates business requirements into technical designs, leads development teams, troubleshoots complex platform issues, and ensures data quality, privacy, governance, and performance. Collaborates with architects and stakeholders on strategy, roadmap planning, delivery, mentoring, and reusable technical standards.

The Infrastructure Design Lead is responsible for leading a cross-functional team, mentoring the DevOps team, driving improvements, and designing hybrid infrastructure solutions that align with architectural standards, emphasizing security and service reliability.

Own the production lifecycle for traditional ML and generative AI systems, including deployment pipelines, model registries, monitoring, retraining, governance, and incident response. Build reliable AWS-based infrastructure for LLM and agentic applications, establish observability and cost controls, and create operational standards, runbooks, and documentation. Partner with U.S.-based Data Science, Data Engineering, and Product teams while independently managing production workloads during India operating hours.

Own revenue operations analytics, reporting, attribution, process design, pipeline management, and workflow automation for a designated revenue segment. Build dashboards, track KPIs, identify growth opportunities, develop business cases, execute initiatives, troubleshoot systems, and partner with leadership on performance and change management.

Design, build, test, and deploy production-grade agentic AI systems using Python, LangGraph, LangChain, AWS Bedrock, and orchestration tools. Integrate AI agents with APIs and business systems, implement memory and retrieval, establish observability and safeguards, and develop reusable SDKs and templates. Collaborate directly with clients to define requirements, run experiments, and rapidly deliver reliable AI solutions using modern software engineering, cloud deployment, and CI/CD practices.

Designs and maintains CI/CD pipelines, cloud infrastructure, infrastructure-as-code, and containerized environments. Improves scalability, reliability, security, monitoring, and automation; troubleshoots infrastructure and application issues; responds to incidents through on-call rotation; and collaborates with development and operations teams to support reliable software delivery.

Designs and maintains a centralized AI platform and agentic framework supporting secure deployment, management, and integration of AI agents. Responsibilities include building scalable agentic solutions, developing unified APIs, integrating agents into applications, optimizing cloud-native workloads, ensuring security and compliance, conducting capacity planning, mentoring engineers, and shaping the platform roadmap.

Designs and maintains a centralized AI platform and agentic framework for secure enterprise deployment and access. Builds scalable AI agents, unified APIs, distributed systems, and microservices; integrates agents into customer and internal applications; optimizes cloud-native workloads; ensures security and regulatory compliance; and leads technical strategy, mentoring, performance analysis, and capacity planning.

Lead backend engineering for a centralized AI platform, designing scalable REST APIs and Python services for agent lifecycle management, deployment, monitoring, authentication, authorization, and asynchronous workflows. Develop data layers and database schemas, contribute to distributed architecture and SDK design, and ensure reliability through testing, CI/CD, and security practices. Mentor engineers, guide technical decisions, and work with modern agentic AI frameworks.

Design, build, operate, and scale reliable cloud-native infrastructure for partner organizations. Manage Kubernetes clusters, CI/CD pipelines, observability platforms, infrastructure as code, and production incident response. Implement SRE practices including SLIs and SLOs, automate operational workflows using Go or Python, support 24/7 on-call coverage, and explore AI, AIOps, and GPU infrastructure. Contribute to technical documentation, presentations, and open-source projects.

Governs material master data through SAP MDG, reviewing and approving CRUD requests, defining data standards and quality metrics, identifying and resolving data quality issues, and reporting governance activities. The role also extracts and catalogs item information, assigns classifications and values, and standardizes spare-parts data. It requires engineering or diploma qualifications, SAP MM training, master data management expertise, data cleansing experience, and knowledge of materials used in industrial sectors.

Leads engineering for ERP data management, high-volume email processing, backend APIs, distributed systems, and service architecture. Owns reliability, scalability, security, observability, and iterative platform improvements for 24/7 systems. Acts as a liaison between engineering and business teams, mentors engineers, and collaborates across global teams in India, China, Singapore, and the United States. The role is fully remote from India and requires flexibility for global operations.

As a Fixed Network Engineer, you will deliver technical support, troubleshoot issues, manage Jira tickets, conduct analysis, and collaborate with teams to enhance product capabilities.

Manage, validate, migrate, and QA/QC environmental data in EQuIS and related systems; configure workflows, build Power BI dashboards, support reporting, automate processes, and prepare documentation and SOPs to ensure data quality and compliance across global projects.

Design, develop, and optimize scalable data platforms, pipelines, and processing workflows using Databricks and cloud technologies. Integrate structured and unstructured data, develop models, implement quality and governance practices, and support analytics, business intelligence, machine learning, and AI use cases. Collaborate with cross-functional teams to translate requirements into technical solutions, troubleshoot production issues, modernize architectures, and improve reliability, performance, and cost.

Coordinate AI and digital transformation programmes by maintaining plans and decision records, preparing government counterpart meetings, drafting funder reports, organizing field visits and training, compiling programme data, and managing documentation. The role requires independent execution, clear writing, government and field comfort, Hindi proficiency, and 30% travel to NRLM states.