Top Tech Jobs & Startup Jobs in Chennai

Build and operate scalable MLOps infrastructure for machine learning development, training, inference, and monitoring. Responsibilities include Kubernetes orchestration, AWS resource management, GPU optimization, prediction and inference platforms, ML workflows, CI/CD pipelines, observability, security compliance, and LLM serving clusters. The role collaborates with ML engineers and supports reliable, high-performance AI services and production operations.

Leads AI innovation across HR SaaS products by owning the AI software development lifecycle, designing agentic and RAG systems, building prototypes, defining production specifications, guiding engineers, and improving in-house AI platforms. Responsibilities include prompt and context engineering, evaluation, monitoring, model optimization, orchestration, governance, and mentoring teams while scaling reliable AI features.

Facilitate high-stakes discussions and negotiations between Japanese- and English-speaking executives. Provide simultaneous and consecutive interpretation, translate and localize business documents, advise on cross-cultural communication and Japanese business practices, and prepare bilingual meeting documentation. Maintain terminology glossaries and support continuity across partnership discussions and project phases.

Develop backend interfaces and APIs integrating Money Forward HR systems with payroll, attendance, accounting, and related services. Design permissions for cross-entity operations, build secure and maintainable backend services, and support testing, documentation, and operations. Collaborate with product and platform teams to deliver scalable end-to-end workflows. The role may also involve frontend development, GraphQL integrations, Kubernetes operations, database work, and applying AI-assisted development tools.

Build and maintain a central banking data platform from scratch, ingesting data from multiple systems and delivering it for management, regulatory reporting, and risk applications. Develop batch and streaming pipelines, transformations, data models, quality validation, reconciliation, governance, lineage, and monitoring using modern AWS and Databricks technologies. Work with increasing independence while collaborating with senior engineers and banking partners.

Build and operate the infrastructure, frameworks, SDKs, runtime systems, and tooling for an AI agent platform supporting ERP product teams. Develop frontend and backend systems, APIs, microservices, distributed services, cloud infrastructure, and production LLM integrations. Translate product requirements into technical solutions, ensure reliability and developer experience, contribute to architecture decisions, and mentor engineers.

Build and maintain reliable, scalable, API-first banking microservices using Kotlin or Java, Spring Boot, SQL, Kafka, and cloud technologies. Collaborate with product owners, master banking domains, write automated tests, deploy code, and support the full software development lifecycle. The role emphasizes secure, high-quality engineering, CI/CD, DevOps/DevSecOps, incident resolution, automation, scalability, and autonomous problem-solving within a digital banking startup.

Leads end-to-end development and operations for large-scale products using Go, cloud-native infrastructure, microservices, and AI-enabled delivery. Builds AI agents, makes architecture and technology decisions, drives rapid prototyping, manages releases, improves system performance and scalability, and establishes automated testing and quality processes. Leads and mentors engineers while coordinating cross-functional stakeholders and measuring business impact through delivery speed, reliability, customer value, and operational efficiency.

Lead and transform a 50-person global QA organization by developing traditional and AI testing strategies, directing performance, scalability, security, and resilience testing, allocating resources, building quality ownership, and mentoring engineers in automation, non-functional testing, and AI validation.

Ensures the quality, reliability, and performance of AI products, model outputs, datasets, and supporting data pipelines. Responsibilities include designing AI/ML test strategies, evaluating prompts and LLM responses, detecting hallucinations and bias, validating ETL/ELT processes and dataset integrity, defining model quality metrics, automating API and data validation tests, integrating tests into CI/CD, and collaborating with engineering, data science, product, and QA teams.