Top Machine Learning Jobs in Hyderabad

Lead technical design, develop secure high-quality production code, drive automation and CI/CD, mentor teams on AI-assisted engineering, ensure operational stability, manage stakeholders, and evaluate vendor/architectural solutions within financial services.

Develop and deploy digital twins, multiphysics and structural models, and physics-informed AI to predict and optimize semiconductor process-structure-performance interactions. Apply computational materials science, ML, GenAI/LLMs, and sparse-data methods to accelerate technology development, yield, and reliability. Collaborate with global engineering teams to translate modeling insights into fab-technology co-optimization.

Lead advanced structure and stress modeling using FEA and level set methods; develop constitutive and thermo-mechanical models; apply scientific computing and ML concepts; use Abaqus/ANSYS for analysis; document results, write reports and collaborate across teams; semiconductor industry experience desirable.

Lead full-chip floor planning, custom analog and mixed-signal layout, physical implementation, verification signoff, and tape-out for advanced memory and high-speed interface designs. Own DRC, LVS, ERC, EMIR, reliability, and manufacturability closure. Define scalable layout methodologies, drive AI-enabled design automation, lead global cross-functional teams, mentor engineers, and deliver advanced-node semiconductor products.

Manage verification for complex DRAM design projects, develop methodologies, lead teams, provide technical advice, and enhance AI-driven verification processes.

Build data-driven solutions and BI using Python, Power BI, and SQL. Implement DevOps pipelines with GitHub Actions/Azure DevOps, deploy on Azure/AWS, integrate APIs, apply unit testing and automation, and collaborate in Agile/Scrum with global teams. Support solution design, effort estimates, and use ML/DL/LLMs for intelligent features.

Lead a team to develop and apply next-generation defect detection equipment and processes, accelerate technology transfer, mentor staff, analyze experiments and defects, build detection techniques, manage cross-functional projects, define program health metrics, and collaborate globally to improve process yield. Data analysis, ML, and statistical modeling expertise is an advantage.

Work in a cross-functional team to develop, implement, and apply numerical and multi-physics models for materials, structures, and processes supporting memory development. Use scientific computing, computational materials methodologies, and ML techniques to analyze results, report findings, and collaborate on next steps. Position is temporary (up to 12 months).

Develop and implement numerical and computational models (structure, materials, process) to support DRAM and NAND node development. Collaborate with process and product teams, create modeling tools and algorithms, analyze results, and present insights to engineering teams and leaders for silicon implementation.

Lead and mentor a data science team, develop ML solutions, drive AI/ML initiatives, ensure model performance and fairness, and foster innovation.

Designs and implements full-stack application features from detailed specifications, collaborates with product and QA, follows coding standards, debugs code, and helps integrate AI-enabled capabilities. May produce installation scripts and contribute to training materials.

Designs, writes, refactors, tests and reviews application and framework components. Mentors peers, delivers training, and estimates work. Develops and maintains tools to automate release processes. Continuously improves coding standards, participates in design, and delivers high-quality, on-time solutions using .NET, Azure, and AI/ML technologies.

Provides engineering-led support for enterprise Azure customers, resolving complex and critical cloud issues, conducting root-cause analysis, optimizing deployments, and managing escalations. Partners with engineering, product, field, and support teams to improve reliability, customer health, troubleshooting guidance, and platform capabilities. Acts as the customer advocate by translating usage patterns and feedback into actionable product and service improvements.

Lead IT business continuity and disaster recovery planning for Roche Informatics. Own continuity plans, recovery repositories, improvement registries, audits, simulations, training, and incident response coordination across global regions. Advise system owners and product managers, drive technical initiatives, collaborate with international stakeholders, and advance AI, machine learning, and automation to improve resilience and operational response in a regulated environment.

Lead and architect software solutions, design and implement features, maintain and tune existing high-revenue products, perform analysis and code reviews, troubleshoot complex problems, and collaborate with stakeholders while participating in Agile/scrum ceremonies.

Lead a software engineering team developing Windows device optimizations across hardware, firmware, operating system, services, and applications. Set technical direction and roadmap, guide complex performance, reliability, debugging, power, and telemetry initiatives, and deliver reliable solutions at scale. Hire, coach, and retain engineers while collaborating with Windows teams, OEMs, silicon vendors, hardware partners, and senior stakeholders.

Analyze telemetry and trace data to diagnose Windows device issues, optimize performance, reliability, power, and compatibility, and implement system improvements across hardware, firmware, operating-system, and application layers. The role involves low-level debugging, architecture leadership, data-driven prioritization, and collaboration with internal teams, OEMs, and hardware partners.

Lead Windows device fundamentals engineering, defining strategy and roadmaps for reliability, compatibility, performance, battery life, and sustainability. Manage complex cross-stack initiatives spanning hardware, firmware, operating systems, services, and applications. Use telemetry, experimentation, debugging, and statistical analysis to guide decisions, while recruiting, coaching, and retaining engineers and partnering with OEMs, silicon vendors, and Windows teams.

Lead and technically drive multiple teams to design, build, and operate large-scale distributed web and cloud services. Influence product vision, adopt AI/ML (including LLMs and agentic frameworks), ensure service health, deliver high-quality results, hire and mentor engineers, and collaborate across product and services groups.

Lead and grow an engineering team responsible for Wi-Fi optimization, device management, provisioning, lifecycle, and cloud connectivity control systems. Own the roadmap, architecture, delivery, reliability, hiring, coaching, and performance of 8–12 engineers. Drive cross-functional execution, customer escalations, standards strategy, design reviews, coding, and large-scale operational decisions across embedded, cloud, and networking systems serving tens of millions of devices.

Leads the design, development, testing, maintenance, and enhancement of scalable Java enterprise software and distributed systems. Creates technical specifications, conducts architecture and code reviews, improves system quality, supports customers, investigates technologies, and mentors developers. The role requires extensive experience with web services, cloud infrastructure, CI/CD, DevOps, big data, testing, security, and database systems, plus the ability to guide technical direction and collaborate across teams.

Lead technical vision and architecture for large-scale, low-latency data systems; contribute hands-on to core code and production support; drive cross-team alignment; mentor engineers; refine data pipelines and CI/CD; apply AI tools to improve workflows; collaborate with Ads and Bing to integrate shared infrastructure and deliver reliable, scalable platforms.

Define technical vision and architecture for large-scale distributed, cloud-based systems. Contribute hands-on to core code, optimize data pipelines, ensure reliability and low-latency performance, mentor engineers, collaborate with product and Ads/Bing teams, resolve production issues, and apply AI tools to improve engineering workflows.

Lead and build massively scalable software and systems; architect low-latency infrastructure; address cybersecurity concerns; apply machine learning with financial engineering to solve complex client problems and drive new product capabilities.

The Software Engineering Lead Analyst will design and implement AI-driven solutions using Python, machine learning, and modern AI tools while enhancing development processes and tools.