Top Machine Learning Jobs in Bangalore

Leads and develops a software engineering team building scalable, secure Data Quality Platform services. Sets technical direction for data profiling, quality rules, data contracts, anomaly detection, alerting, and incident workflows. Partners cross-functionally on roadmaps, architecture, delivery, and resource trade-offs. Establishes engineering standards and ensures reliable production operations through observability, incident management, capacity planning, and continuous improvement. Guides practical use of AI and machine learning to improve data quality workflows.

Own customer service requests from contract validation through resolution, including emergency network restoration, SLA and KPI oversight, proactive fault prevention, data-driven support, and customer-network software implementations. Diagnose unsuccessful software loads, implement corrective actions, improve support processes, and build customer relationships through consultation, negotiation, knowledge sharing, and change management.

Perform end-to-end system verification of Ericsson 5G RAN solutions, including capacity, performance, scalability, mobility, and high-load traffic testing. Analyze logs, traces, KPIs, and protocol-layer behavior; troubleshoot PHY/MAC/RLC/PDCP/IP issues and identify root causes. Support feature integration, test automation, verification frameworks, and CI/CD improvements. Collaborate with development, integration, CI, and product teams while applying modern and AI-assisted verification methods.

Leads software engineering delivery within an agile team, designing, developing, testing, debugging, and supporting secure, scalable applications. Establishes effective and responsible AI-assisted engineering practices, including code generation, review, testing, troubleshooting, and validation. Automates recurring remediation, improves operational stability, evaluates vendor and startup technologies, and contributes to AWS web application development, Java engineering, cloud deployments, DevOps, and machine learning or LLM integrations.

Design and build scalable SDET testing infrastructure for data connectors, parsers, and ingestion platforms. Develop reusable testing frameworks, CI/CD integrations, AI-powered test generation, monitoring and reporting systems, utilities, and testing standards. Lead technical architecture decisions while ensuring quality, reliability, and accuracy across distributed systems. The role requires programming expertise in Python or Go, cloud platform experience, containerization, orchestration, and scalable automation framework development.

Leads validation strategy and automated testing for NAND-based SSD firmware across functional, performance, reliability, interoperability, and customer qualification domains. Develops test plans and automation frameworks, debugs NVMe and SAS issues, analyzes quality risks, and drives issue closure with cross-functional engineering teams and hyperscale customers. Applies AI and machine learning to test generation, failure analysis, anomaly detection, reporting, and predictive quality. Mentors engineers and leads complex validation programs.

Designs, develops, maintains, and improves scalable software applications and systems. Leads AI adoption across software deployment automation, collaborates with product and engineering teams, troubleshoots complex issues, participates in code reviews, and documents applications and deployment processes. Creates training, demonstrations, workshops, playbooks, reusable patterns, and use cases to help engineering teams and business stakeholders adopt AI solutions.

Leads validation strategy and automated testing for NAND-based SSD firmware across functional, performance, reliability, interoperability, and customer qualification domains. Develops test plans, frameworks, and continuous validation solutions; debugs NVMe and PCIe issues; analyzes quality risks; and drives cross-functional issue resolution. Partners with hyperscale customers, leads complex programs, mentors engineers, and applies AI, machine learning, and generative AI to improve test coverage, failure analysis, reporting, and validation productivity.

Design, build, and maintain backend distributed systems and core infrastructure services (configuration, rate limiting, feature flags, A/B testing). Improve performance, scalability, and observability of cloud infrastructure. Deliver cross-team, deadline-sensitive features end-to-end and influence the infrastructure roadmap.

Build end-to-end internal audit applications and dashboards to automate control testing and continuous risk monitoring. Integrate data pipelines, LLMs/ML, and cloud-native microservices to deliver near real-time risk insights and partner with auditors and data engineers on telemetry and deployments.

Lead engineering teams to design and deliver cloud-native, large-scale full-stack and microservices solutions on AWS/Azure/GCP; collaborate with product and analytics to build intelligent data products; mentor engineers, evaluate code, and drive technical strategy using Kubernetes and serverless infrastructure.

Develop and maintain scalable applications using Java and/or Python, optimize SQL and database solutions, build REST APIs and microservices, manage cloud deployments and CI/CD pipelines, and integrate AI/ML and generative AI capabilities. The role includes testing, code reviews, production troubleshooting, Agile collaboration, and cross-functional work with data scientists, architects, and business stakeholders.

Designs, builds, and configures Oracle Sales Cloud applications while leading AI-enabled delivery and complex project recoveries. Responsibilities include solution design, implementation, testing, sprint coordination, governance, risk and issue management, schedule and budget tracking, stakeholder communications, quality improvement, contract deliverables, and team coaching. Requires extensive Oracle Fusion Sales and Incentive Compensation expertise, GenAI and Agentic AI experience, Agile delivery knowledge, and the ability to stabilize troubled transformations and migrations.

Leads complex Oracle integration and application delivery initiatives, including solution design, implementation, testing, AI enablement, project recovery, governance, risk management, financial tracking, and stakeholder communications. The role requires hands-on Oracle Integration Cloud and Oracle VBCS expertise, GenAI and agentic AI experience, Agile delivery leadership, and the ability to stabilize troubled programs, manage teams, improve quality, and execute transformation or migration initiatives.

Supports embedded software developers, testers, and leads in designing, developing, testing, verifying, and documenting automotive software for electrification products. Works within AUTOSAR, ASPICE, functional safety, and cybersecurity standards. Contributes to automation, proof-of-concept, and advanced projects that improve engineering efficiency and quality. Applies C, Python, automotive communication protocols, software testing, and emerging AI technologies to embedded software and engineering processes.

Design, build, and operate observability, anomaly detection, and reconciliation tooling for monetization systems. Implement metrics/logs/traces, automated billing and data anomaly detection, SLOs, runbooks, incident response, and collaborate with product, finance, and support. Leverage telemetry and machine learning to predict and resolve monetization issues, and guide technical direction for reliability and detection work.

Build scalable backend APIs, data pipelines, orchestration layers, and front-end dashboards powering conversational AI agents. Integrate AI and machine learning models with enterprise workflows, ensuring real-time performance, security, reliability, and low latency. Develop human-in-the-loop workflows, support enterprise-ready product features, and collaborate with AI/ML engineers and architecture leaders. Contribute to code reviews, system design, and engineering best practices in a fast-paced startup environment.

Own the architecture, design, development, deployment, and support of Workday Extend applications and enterprise integrations. Lead solutions using Workday Studio, APIs, AWS, AI, and Generative AI while establishing security, integration, testing, observability, and CI/CD standards. Partner with cross-functional stakeholders, conduct technical discovery and architecture reviews, identify risks, and mentor developers. The role drives modernization of Okta’s Workday technical landscape and delivers scalable, secure, maintainable employee technology solutions.

Lead the design, development, and operation of internal PaaS offerings, including API gateways, Kafka platforms, portals, dashboards, backend services, databases, and CI/CD systems. Drive AIOps and No-Ops initiatives using machine learning, architect resilient distributed systems, provide on-call support, establish engineering standards, and mentor junior engineers. Collaborate with infrastructure, SRE, and software engineering teams to deliver scalable, secure, and highly available platforms.

Leads the technical direction, architecture, design, and delivery of custom robotics software solutions. Oversees development standards, code quality, scalability, performance, lifecycle management, and cross-functional integration. Acts as a subject matter expert, makes team-level technical decisions, mentors engineers, resolves complex challenges, and drives continuous improvement across development processes.

Design and develop scalable backend services, APIs, integrations, data ingestion solutions, and AI-enabled applications using Python or TypeScript. Deploy and operate production systems on AWS with CI/CD, while improving reliability, testing, monitoring, security, performance, and maintainability. Contribute to architectural decisions, technical direction, and engineering standards. Preferred experience includes distributed systems, containers, infrastructure as code, observability, workflow orchestration, MLOps, streaming, LLM integrations, prompt engineering, and retrieval-augmented generation.

Leads engineering teams building scalable advertising and marketing technology platforms serving millions of users. Responsibilities include architecture, code reviews, technical strategy, modernization to cloud-native microservices, performance, observability, resiliency, and platform transformations. The role also develops global engineering talent, influences organization-wide standards, and partners with product, analytics, architecture, and program leadership.

Develop artist-facing and backend tools that extend SideFX Houdini and integrate with VFX and CG production pipelines. Build clean, testable, maintainable software using HOM, HDK, Python, and/or C++. Collaborate with artists, developers, and project managers across the software lifecycle, while emphasizing usability, documentation, UX, reliability, and adoption. Support procedural workflows, production tools, simulations, rendering, and distributed studio systems.

Build Python-based tools for layout, animation, matchmove, and previs artists working with complex CG scenes. Develop scene-management systems, asset and animation library integrations, and 3DEqualizer data import/export workflows. Collaborate with product owners and stakeholders in an Agile environment, contributing across design, development, maintenance, and pipeline integration. Work may include front-end and back-end development for high-end VFX and feature animation pipelines.

Develop artist-facing and backend tools that extend Autodesk Maya and integrate with VFX and CG pipelines. Build reliable, maintainable software using HOM, HDK, Python, C++, MEL, and Maya APIs; improve workflows, usability, and adoption; and collaborate with artists, developers, and project managers throughout the software lifecycle. The role also involves documentation, testing, deployment, support, roadmap planning, and innovation across distributed VFX production teams.