Top Python Developer Jobs in Delhi

Leads customer-facing design and implementation of cloud, platform, infrastructure, data, and AI solutions. Responsibilities include cloud migration and modernization, Infrastructure as Code, CI/CD, Kubernetes, automation, GitOps, platform security, observability, and AI-assisted development. The consultant conducts workshops, creates reusable platform capabilities, enables responsible AI adoption, mentors engineers, supports proposals and pre-sales, and ensures secure, reliable, governed, and cost-effective production solutions.

Develop and maintain ScienceLogic’s internal developer platform, including Kubernetes environments, CI/CD pipelines, container tooling, and infrastructure-as-code. Collaborate with development teams to understand workflows, enforce software engineering and container best practices, evaluate technologies, and contribute to architecture decisions. The role also involves documenting standards, supporting observability and logging, and implementing DevSecOps practices such as secure build pipelines and compliance automation.

Lead strategic design, architecture, and hands-on implementation of Elastic Stack and Search AI solutions for enterprise customers. Conduct workshops, gather requirements, develop proofs of concept, optimize queries and clusters, and deliver scalable search platforms. Manage project objectives, dependencies, timelines, and customer relationships. Collaborate with engineering, product, support, and sales teams, provide product feedback, assess technical risks, and mentor team members. Periodic client-site travel of up to 20% is required.

Design and implement scalable Elastic Stack observability and Search AI solutions for enterprise customers. Lead workshops, requirements gathering, architectural planning, proofs of concept, data modeling, query optimization, cluster tuning, and end-to-end project delivery. Advise customers, manage engagement timelines and dependencies, collaborate with engineering and product teams, support presales scoping, and mentor consultants. The role requires hands-on cloud, infrastructure, networking, security, containerization, automation, and programming experience, with periodic client-site travel up to 20%.

The Mapping City/Country Expert improves transit data and advises on urban and multi-modal transport, creating data policies and communicating insights to stakeholders.

Design, build, test, and maintain responsive front-end and back-end applications; develop APIs, databases, and security settings; debug and optimize software; support mobile-responsive features; write technical documentation; collaborate with data scientists/analysts and maintain the HRMS.

Design, build, and maintain backend APIs and services for a real-time supply chain visibility platform. Own system architecture, real-time event ingestion, data normalization, ML integration, monitoring, and production debugging while collaborating with Product and Data Science to deliver scalable, high-availability features.

Deploy alongside clients to transform heterogeneous multimodal data into semantic structures for AI reasoning. Design ontologies, segmentation, embedding and retrieval strategies, collaborate on ingestion pipelines, evaluate retrieval and reasoning performance, and translate insights into product and engineering signals.

Operate and own model lifecycle for defence and strategic deployments: design serving infra, CI/CD for model updates, monitoring/observability, evaluation/A-B testing, containerised serving for edge and air-gapped environments, collaborate on eval pipelines, create runbooks, and lead incident response for production model failures.

Design, build, and operate terabyte-scale data storage and ingestion platforms for on-premise and air-gapped AI deployments. Implement indexing, partitioning, query optimisation, pipeline orchestration, observability, capacity planning, and infrastructure automation while collaborating with data scientists and product teams.

Own end-to-end deployment of Sarvam's full AI stack in on-prem, air-gapped, and classified client environments. Act as technical SPOC from scoping to steady-state, diagnose integration and inference failures, manage deployment pipelines and model serving, drive adoption via training/documentation, and feed field learnings back into product.

Design, build, and maintain scalable backend and frontend systems using Python and modern frameworks; lead and mentor developers; manage delivery roadmaps; integrate frontend components; ensure performance, security, and production stability; and apply AI/ML techniques where applicable.

Own the architecture and technical direction of large-scale distributed applications. Build secure, scalable Python backend services, contribute to Java services, guide platform strategy, mentor engineers, and establish engineering standards. Improve reliability, observability, automation, developer experience, security, testing, and operational excellence. Use AI-assisted development tools responsibly to increase productivity while maintaining software quality. Collaborate cross-functionally and lead through technical excellence, pragmatic architecture, and strong ownership.

Own enterprise AI engagements from discovery through production deployment. Design, build, integrate, debug, evaluate, and harden agentic systems using LLMs, RAG, tool calling, workflows, and guardrails. Lead technical workshops, proofs of concept, solution scoping, and executive ROI discussions. Measure adoption, containment, accuracy, and cost outcomes; create reusable delivery assets; enable partners; and provide product feedback. The role is customer-embedded and requires sustained on-site presence during engagements.

Architect and operate scalable, reliable infrastructure across Kubernetes, AWS, RDS, and distributed microservices. Lead capacity planning, benchmarking, observability, alerting, SLA enforcement, security, CI/CD, and infrastructure roadmap initiatives. Apply AI and automation to improve reliability and developer productivity. Optimize databases and production systems, identify bottlenecks, mentor engineers, and translate business objectives into technical roadmaps. The role requires strong Golang, backend, SRE, distributed systems, database, and cloud expertise.

Manage daily Data team operations, research and analyze datasets, build internal and external reports (including an annual Startup Ecosystem Ranking), and maintain/improve Python scripts and data infrastructure to deliver actionable insights.

Lead data science efforts by analyzing large datasets, developing and deploying ML models, building scalable data pipelines, designing experiments (A/B testing), creating visualizations, and mentoring junior team members to drive data-driven decisions.

Design, build, and operate scalable batch and real-time data pipelines and analytics platforms. Partner with enterprise customers to define requirements, own technical delivery from discovery to adoption, and present architectures to stakeholders. Drive data governance, observability, CI/CD, and platform optimization while mentoring junior engineers and collaborating with Product, Engineering, and Customer Success teams to enable analytics and AI/ML applications.

Lead the Monetization Infrastructure engineering team, provide hands-on technical leadership, ensure high uptime and incident management, collaborate with Finance and Product, drive operational excellence, and champion adoption of AI-assisted SDLC practices.

Design and implement pharmaceutical sales incentive compensation plans, including forecasting, goal setting, simulations, payout calculations, scorecards, and call planning. Analyze sales data using Python, SQL, and Excel to evaluate performance and generate reports. Lead operational teams, manage client deliverables, improve processes, mentor team members, and support commercial operations compliance and best practices.

Design, implement, and maintain AWS-based CI/CD solutions using Jenkins. Manage build environments, operating systems, software, and multiple AWS accounts. Collaborate across teams to develop DevOps solutions, promote agile practices, and troubleshoot CI and production issues. The role also involves applying AI tools, coding assistants, agents, MCPs, and agentic SDLC practices to improve testing, troubleshooting, and workflows.

Design and deliver intelligent platforms, automation solutions, and AI-driven capabilities. Analyze complex production systems, identify failure modes, and improve reliability, efficiency, productivity, and mean time to recovery through AI, automation, operational intelligence, and modern engineering practices. Build scalable cloud-native solutions using Kubernetes, APIs, MLOps, generative AI, and workflow orchestration while mentoring teammates and driving continuous improvement.

Oversee, scale, and optimize high-density AI hardware infrastructure across regional data centers. Build Python automation and infrastructure-as-code tooling, integrate incident workflows, develop telemetry pipelines and monitoring dashboards, and improve reliability across private cloud, bare-metal, and virtualized environments. Lead on-call incident response, runbooks, post-mortems, service rollouts, vendor coordination, and field technician activities while driving uptime, performance, and operational readiness.

Develop Python applications, APIs, automation tools, and platform capabilities while supporting CI/CD, Docker, Kubernetes, cloud and on-premise deployments. Implement observability, monitoring, alerting, and SRE practices including SLIs, SLOs, error budgets, incident response, disaster recovery, and root cause analysis. Troubleshoot production systems, automate recurring operational work, maintain documentation, and collaborate across engineering, security, platform, product, operations, and external teams.

Provide L3 support and platform engineering for the Domino Data Lab platform. Troubleshoot production incidents involving workspaces, models, jobs, connectivity, access, and performance; conduct root-cause analysis and implement permanent fixes. Manage upgrades, patching, monitoring, tuning, and reliability for enterprise data science and AI/ML workloads. Support Python and R environments, ML workflows, Kubernetes, Docker, AWS, SageMaker, and Azure Machine Learning integrations in regulated or life sciences environments.