Top Machine Learning Jobs in Bangalore

Lead an 8-10 engineer team owning courier systems (dispatch, matching, pricing, ETA). Set technical direction, drive data-driven decisions and experiments, partner with data science, ensure reliability and scalability of low-latency distributed systems, and own hiring, performance, and operational excellence.

Senior software engineer responsible for designing, developing, and deploying IIoT device and network management features. Lead full lifecycle development, mentor engineers, collaborate cross-functionally, apply AI-first approaches, and ensure scalable, secure, real-time edge and cloud solutions.

Develop and maintain high-performance, scalable risk metrics engines and workflows. Write production-grade backend code, implement cloud-first architectures (AWS), integrate AI-enabled tools, follow CI/CD and testing best practices, and collaborate with quantitative engineers and product managers to support firm-wide risk, liquidity, and trading analytics.

Lead architecture and development of the core native iOS SDK and related apps. Own feature delivery from design to public release, write high-quality tested code, collaborate with R&D and ML teams, support customers to resolve issues, and manage priorities across large-scale, distributed projects.

Lead SRE for the Network Assurance Data Platform responsible for designing, building, and optimizing cloud and big-data infrastructure for ML/AI workloads. Provide technical leadership, troubleshoot production issues, implement SRE practices (monitoring, alerting, error budgets), mentor engineers, collaborate with cross-functional teams, and drive the platform roadmap and automation for enterprise-scale deployments.

Lead and manage a team of Software Engineers focusing on building tooling for internal teams at Nike, emphasizing collaboration and modern software practices.

The Director of Software Engineering will lead teams in developing highly available distributed systems, focusing on payment processing, AI solutions, and innovative capabilities, while ensuring product quality and team engagement.

The role involves developing enterprise applications using Java and Spring, integrating AI features, mentoring junior developers, and collaborating with cross-functional teams.

Lead the architecture of physical implementation for high-speed CPU designs, focusing on automated ML-driven methodologies and PPA optimization, while interacting with EDA partners.

Lead full lifecycle development of L2/L3 embedded RAN protocol stacks: design, coding, unit testing, debugging, and integration. Implement and enhance MAC/RLC features, analyze logs, debug in Linux, and support product releases. Apply AI/ML concepts for RAN optimization and develop/maintain solutions used by global customers.

Lead and technically drive Nike's multi-cloud platform engineering: design, build, operate, and govern cloud infrastructure solutions (Kubernetes, control plane, CRDs, autoscaling). Split time between hands-on engineering and leadership—shaping technical priorities, best practices, partnering cross-functionally, and mentoring cloud engineering teams.

Create and review realistic, domain-specific robotics tasks in Hindi; apply scoring rubrics; evaluate AI and human responses for accuracy, regulatory alignment, and professional quality; and produce gold-standard solutions and expert feedback.

Lead a cloud infrastructure engineering team building and operating Nike's multi-cloud platform. Split time between hands-on design, development, and operations of cloud solutions and leadership duties: setting technical priorities, defining best practices, coaching engineers, and partnering with cross-functional teams to deliver scalable, secure cloud architectures.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

You will design ML models to improve search and personalization, mentor junior engineers, and write maintainable code while collaborating with customers.

Lead and manage a team of ML engineers, drive AI service delivery, and oversee production model deployment while fostering a high-performance culture.

The ML QA Lead ensures quality in Cerebras software across ML workloads, focusing on testing accuracy, performance, and usability. Responsibilities include driving software quality, automating workflows, debugging issues, and collaborating across teams.