Top Artificial Intelligence Jobs in Hyderabad

Design and implement the MCP/ACP ecosystem for AI tools, focusing on orchestration patterns, security, and cross-functional collaboration to ensure scalable production operations.

The Lead Software Engineer will design, develop, and implement complex software solutions while providing technical leadership and mentoring to junior engineers, ensuring high-quality delivery and adherence to software development best practices.

Design and own RTL development for an AI-enabled RISC-V CPU core, including microarchitecture, vector/matrix engines, performance/power/area optimization, and verification support. Collaborate with cross-functional teams for timing, reliability, testability, and SoC integration.

Architect and deliver RCM and AI-driven automation solutions, translating business needs into workflows, Agile epics, and user stories. Lead development teams, sprint delivery, ROI analysis, GitHub repositories, CI/CD pipelines, testing, bug resolution, production support, and quality assurance. Mentor business analysts and developers while improving scalable system reliability and business outcomes.

Design, develop, and maintain scalable automation frameworks and test suites. Integrate automated tests into CI/CD pipelines, leverage AI/ML or intelligent automation to improve coverage and speed, analyze requirements, track and resolve defects, collaborate with QE leads and developers, and mentor junior engineers.

Lead the development of AI-powered customer support solutions, guiding a team to build scalable, reliable, and secure systems using LLMs and third-party integrations.

Leads customer-facing design and implementation of cloud, platform, infrastructure, data, and AI solutions. Responsibilities include cloud migration and modernization, Infrastructure as Code, CI/CD, Kubernetes, automation, GitOps, platform security, observability, and AI-assisted development. The consultant conducts workshops, creates reusable platform capabilities, enables responsible AI adoption, mentors engineers, supports proposals and pre-sales, and ensures secure, reliable, governed, and cost-effective production solutions.

Develop and maintain AI-assisted development capabilities for the Eclipse-based SmarTest semiconductor testing environment. Integrate and optimize GitHub Copilot, resolve Eclipse and integration defects, implement customer-specific features, and perform testing and quality assurance. Translate customer requirements into technical solutions, provide technical assistance, evaluate changes in AI and Eclipse technologies, and collaborate with Microsoft, Advantest teams, and external partners to improve developer productivity and user experience.

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.

Lead post-launch operations for Sales AI applications, ensuring availability, performance, security, governance, support readiness, and continuous improvement. Establish incident, problem, change, release, and lifecycle processes; monitor service and adoption metrics; develop automation, observability, self-service capabilities, and production-readiness practices. Partner with Sales, Product, Engineering, Data, Security, and IT teams to improve user experience, platform reliability, adoption, and business value.

Lead end-to-end development of full-stack applications using Java and Angular. Design REST APIs, integrate databases, improve backend performance and security, participate in architecture and code reviews, troubleshoot production issues, mentor junior engineers, and promote clean coding practices in an Azure cloud environment.

Leads end-to-end development of cloud-native full-stack applications using Java and Angular. Designs REST APIs, integrates databases, ensures performance and security, participates in architecture and code reviews, troubleshoots production issues, mentors junior engineers, and drives clean code and engineering quality. The role requires extensive software engineering experience, Azure expertise, SQL Server, CI/CD, Git, and Docker.

Design and maintain scalable Java and Spring Boot backend services, REST APIs, microservices, and event-driven systems. Build cloud-native applications using Docker, Kubernetes, and AWS while ensuring reliability, security, observability, and performance. Use AI-assisted development tools for coding, testing, debugging, documentation, and reviews. Participate in architecture discussions, production support, incident response, and on-call rotations. Collaborate across engineering and product teams and mentor other engineers.

Lead design, customize, and implement ServiceNow ITSM, Service Portal, and ITOM solutions. Perform integrations (REST, SOAP, MID Server), Discovery and CMDB maintenance, and develop/optimize scripts. Run workshops, mentor junior developers, and participate in Agile delivery to translate client requirements into scalable ServiceNow solutions.

Develop automation tooling and scripts (Python/Go/Bash) to eliminate manual operational toil. Manage Kubernetes deployments with Helm, automate DB operations across SQL/NoSQL, maintain CI/CD and GitOps pipelines, implement observability and self-healing infrastructure, and apply software engineering practices to infrastructure code.

Design, build and ship native Android features for an AI-enabled neobank using Kotlin. Integrate backend APIs, improve performance and reliability, collaborate with product and design to simplify financial user journeys, and apply on-device ML where it improves UX.

Build and ship native Android features in Kotlin and Jetpack Compose; integrate REST APIs; improve performance, crashes and memory; collaborate with product/design; use analytics and on-device AI (TensorFlow Lite) to enhance user journeys for insurance and financial services.

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

The AI QA Specialist will design and execute test strategies for AI systems, validate model outputs, implement monitoring dashboards, and ensure compliance with safety standards.

Design, build, and operate high-throughput, low-latency backend services and AI/ML pipelines on an event-driven platform. Own Go services, WebSocket backends, RAG/embedding/model-serving components, and connectors to CCaaS/CRM/telephony. Work in 1-week sprints, deliver production-grade integrations or AI features, and meet strict latency and delivery commitments.

Build end-to-end software features across frontend, backend, and AI components. Responsibilities include developing interfaces, APIs, backend services, data pipelines, automation workflows, and LLM-powered applications such as RAG systems and AI agents. The role involves testing, documentation, debugging, cross-functional collaboration, product iteration, and using AI coding tools to improve development speed and quality.

Lead and mentor engineering teams to design, build, and deliver scalable, secure, AI-enabled cloud-native applications. Own architecture, drive technical strategy, implement CI/CD and IaC, contribute hands-on to APIs, microservices, integrations, and AI/LLM initiatives while engaging stakeholders and ensuring operational excellence.

Design and execute test plans for web, Salesforce, and enterprise applications; perform functional, regression, integration, and end-to-end testing; build and maintain Playwright automation frameworks; integrate tests into CI/CD; track defects; support performance/security testing; mentor junior QA engineers and promote quality best practices.

Lead ownership of end-to-end development for one or more product modules, designing and building scalable SaaS services using .NET/C#, REST APIs, SQL/NoSQL, and Flutter/React. Collaborate with product, design, and engineering teams; manage full software lifecycle, deploy and monitor services in cloud (AWS/Azure); leverage AI-assisted development tools responsibly; build reusable engineering assets and automation to improve productivity and reliability.

Serve as a senior solutions engineer for Identity & Fraud products: advise delivery/support teams across the SDLC, provide operational guidance, product consultancy, incident support, and drive AI-native automation. Deliver and troubleshoot SaaS and on-prem solutions, implement cloud/DevOps/CI-CD practices (AWS, Git/Jenkins/Ansible/Terraform), monitoring (Splunk, DynaTrace), container/orchestration platforms (Docker, Kubernetes, EKS), Kafka and API testing tools, while documenting and advocating customer-driven improvements.