Top Engineering Jobs in Chennai

Lead development of cross-platform React Native mobile applications for iOS and Android. Own features from technical design through deployment, monitoring, and iteration; guide architecture, performance, testing, security, CI/CD, and native integrations using Swift and Kotlin. Mentor engineers, lead code reviews, establish development standards, and collaborate with Product, Design, Backend, QA, and POS teams. Incorporate AI coding assistants responsibly into daily engineering workflows.

Design and develop large mining thickening equipment using SolidWorks. Create and validate 3D models, drawings, BOMs, and documentation. Ensure design quality, manufacturability, and compliance with standards. Support fabrication, assembly, site teams, and contribute to product improvement and value engineering.

Lead product standardization for mining thickening equipment: develop and approve 3D models, drawings, BOMs; create standard parts and libraries; drive design optimization, modular development, and continuous improvement; mentor designers; collaborate with manufacturing, sourcing, and quality to deliver compliant, cost-effective engineering solutions.

Manages an application engineering team, including workload, performance, KPIs, quotations, and technical training. Reviews control valve specifications and proposals, establishes selection and order-entry guidelines, supports complex projects, and leads technical and commercial customer engagements. Partners with Sales and Industry teams on product selection, new opportunities, and continuous improvement. Oversees high-priority projects through post-order activities and shipment while maintaining stakeholder satisfaction and improving team quality and productivity.

Leads enterprise-wide data and AI strategy for drug development, including platform roadmaps, architecture, GenAI and agentic frameworks. Directs multidisciplinary engineering teams from prototype through compliant production delivery; establishes data governance, AI/ML controls, API and event-driven integration standards, and DevOps, DataOps, and MLOps practices. Oversees secure, traceable, monitored AI and RAG solutions while influencing technology choices, vendor partnerships, and engineering standards across the organization.

Leads enterprise Data and AI strategy for drug development, including platform investments, architecture, GenAI, agentic systems, governance, and production engineering standards. Oversees multidisciplinary teams delivering secure, compliant AI-native products, scalable data platforms, APIs, automated pipelines, and monitoring frameworks. Establishes controls for data integrity, ML governance, RAG reliability, security, and operational performance while mentoring engineers and influencing senior stakeholders.

Develop, enhance, test, deploy, and support full-stack software applications. Apply expertise in programming, databases, secure coding, design patterns, documentation, and data security. Collaborate with architects, product managers, project teams, and end users to solve complex business problems. Research and adopt new technologies while supporting risk controls, data retention, and recovery requirements.

Lead multiple engineering teams and shape the technical direction of a major product area. Responsibilities include developing engineering managers, guiding architecture, driving cross-functional execution, defining strategy, planning staffing and succession, and partnering with product, design, data science, and executive leaders. The role requires deep technical judgment, experience with large distributed systems, and the ability to scale engineering organizations while maintaining strong engineering culture and execution.

Design, develop, and optimize AI, SLM/LLM, and agentic solutions. Implement and tune algorithms and large-scale distributed training, orchestrate model fine-tuning and serving, analyze performance, define APIs, and communicate technical outcomes to non-technical stakeholders.

Supports and administers middleware platforms including Tomcat, WebSphere, Liberty, Kafka, IBM BPM, and IBM MQ in Linux environments. Responsibilities include installation, configuration, troubleshooting, performance tuning, security patching, access management, monitoring, high availability, disaster recovery, application integration, and production support across development through production environments. The role also follows ITIL and ITSM processes, coordinates with vendors, and participates in 24/7 operational coverage.

Leads ASIC package design from planning through implementation, optimizing pinouts, stack-ups, power distribution, and high-speed routing. Collaborates with floor planning, signal integrity, power integrity, layout, and vendor teams to deliver manufacturable, high-performance packages. The role also improves design methodologies, coordinates stakeholders, drives innovation, and mentors designers. Experience with EDA tools, package design, testing equipment, and scripting is valuable.

Designs and develops scalable, secure Java payment applications for banking and fintech systems. Responsibilities include technical solutioning, architecture reviews, coding, testing, CI/CD, production troubleshooting, root-cause analysis, and system modernization. The role requires expertise in payment messaging standards, enterprise integrations, databases, messaging platforms, cloud-native infrastructure, observability, and secure data flows. AI and LLM tools are used to accelerate development, testing, documentation, and operational improvements.

Coordinates engineering change requests from initial assessment through approval and implementation. Facilitates technical and organizational review meetings, prepares agendas and summaries, supports engineers using CMII and GES, and ensures compliance with documentation, configuration management, quality, and change-control standards. The role also contributes to engineering databases and tools while collaborating with cross-functional teams.

Owns end-to-end client activation for AI-powered SaaS solutions, including discovery, solution design, workflow setup, demonstrations, training, performance monitoring, and optimization. The role serves as the primary client contact, translating business needs into practical AI workflows and partnering with technical specialists on complex customizations. It requires strong communication, consultative client management, AI tool familiarity, and experience in client success, implementation, or technical consulting.

Lead QA Engineers, SDETs, and Qualification Engineers while defining quality strategy, test automation, and product qualification efforts. Drive functional, integration, system, regression, and end-to-end testing; build scalable automation frameworks; integrate testing into CI/CD; monitor quality metrics; improve defect prevention and release readiness; and advance AI-assisted testing. The role is approximately 60% hands-on technical work and 40% people management.

Leads platform-level execution of software and AI/ML systems across multiple engineering teams. Oversees shared infrastructure, DevOps, CI/CD, cloud-native architecture, cost optimization, system integration, and full software lifecycle delivery. Partners with product and data teams, guides architectural decisions, mentors engineers, manages stakeholders and technical initiatives, and drives system availability, scalability, maintainability, and continuous improvement.

Leads the Supabase Spark engineering team by developing people, managing hiring, owning roadmap delivery, coordinating cross-team requests, maintaining service objectives, and handling incidents. The role requires technical credibility in backend and database systems, experience managing remote engineering teams, strong performance management skills, and the ability to build high-trust, asynchronous, globally distributed teams.

Develop, qualify, and sustain optical assembly processes for high-speed transceivers, photonic modules, optical engines, and communication systems. Responsibilities include fiber preparation, fusion splicing, routing, connector integration, optical alignment, packaging, measurement, reliability testing, process control, yield improvement, root cause analysis, NPI support, and transition to high-volume manufacturing.

Lead AI and machine learning engineering activities for customers, partners, and prospects. Advise on GenAI/ML solutions, architect end-to-end workflows from data ingestion through deployment, lead proofs of concept, support customer delivery, and manage and mentor a team of AI and machine learning engineers. The role also involves pre-sales support, consultative solution design, technical presentations, and stakeholder engagement.

Leads enterprise modernization of legacy data platforms, ETL pipelines, and data warehouses into scalable cloud-native architectures. Defines target-state architecture, migration roadmaps, engineering standards, reusable frameworks, and modernization patterns. Requires hands-on expertise with Databricks, Microsoft Fabric, Azure Data Factory, lakehouse architecture, data modeling, CDC, streaming, governance, data quality, automation, and deployment frameworks. The role also assesses existing environments, optimizes performance, and delivers enterprise-scale migration projects.

Administer and support large-scale cloud and on-premises monitoring deployments across multiple providers. Maintain performance, availability, capacity, permissions, upgrades, and audit compliance. Integrate applications and infrastructure with enterprise monitoring instrumentation, document operational processes, collaborate with technical teams, and mentor colleagues on observability practices. The role requires expertise in monitoring platforms, application performance monitoring, synthetics, end-user experience, infrastructure monitoring, distributed tracing, cloud services, scripting, SQL, and multiple operating systems.

Lead and scale the engineering organization for a regulated digital-asset custody platform. Own hiring, org design, engineering operations (on-call, incidents, release hygiene), delivery against roadmaps, audit and regulator readiness (SOC 2, SAMA, ISO 27001), and cross-functional alignment. Amplify an existing small, specialized team to meet regulatory and institutional requirements while preserving culture and retention.

Leads a 3–6-person AI-focused engineering team while contributing at least 50% directly to software development. Responsibilities include architecture, full-stack coding, code reviews, testing, documentation, mentoring, delivery, and collaboration with product, design, and QA teams. The role requires commercial LLM experience, strong C# and JavaScript development skills, knowledge of databases, REST APIs, microservices, cloud platforms, and software engineering management.

Lead a 10–15-person engineering team building agentic AI systems from the ground up. Set architecture strategy for planning, memory, tool use, multi-agent coordination, guardrails, observability, evaluations, reliability, and safety. Drive adoption of an AI-Driven Development Lifecycle, partner with product managers on ambiguous requirements, oversee delivery and technical reviews, and support hiring, mentorship, career growth, and leadership reporting.

Leads backend and platform engineering teams responsible for API-first product platforms and AI usage metering. Owns hiring, development, delivery, platform reliability, API governance, usage accounting, and operational excellence. Partners with Product, Platform, SRE, Finance, Revenue Operations, and Pricing on roadmaps, entitlements, quotas, billing integrations, and reporting. Guides architecture and technical direction while developing senior engineers and maintaining predictable outcomes across globally distributed teams.