Top Artificial Intelligence Jobs in Chennai

Design and develop responsive web applications and reusable UI systems while building AI-powered experiences using GenAI and LLM technologies. Integrate AI platforms and APIs, develop chatbots and copilots, implement prompt engineering and RAG, and optimize applications for performance, scalability, security, and usability. Collaborate with product, design, data science, and backend teams, contributing to testing, deployment, code reviews, and production support.

Evaluate relevance of filters and options on desktop and mobile, ensuring they match customer queries and preferences. Follow guidelines, conduct online research, verify assessments for accuracy and consistency, and complete required training and qualification tests before starting. Work remotely on a flexible, part-time freelance schedule.

Develop and maintain custom ERPNext and Frappe applications, including DocTypes, workflows, reports, dashboards, print formats, integrations, and APIs. Diagnose bugs, improve performance, maintain existing customizations, and manage deployments through Git and staging environments. Use and review AI-generated code for implementation, debugging, testing, refactoring, security, and maintainability. Communicate progress and clarify requirements while supporting ERPNext workflows and related infrastructure.

Develop end-to-end generative AI solutions using LLMs, NLP, computer vision, audio, and multimodal technologies. Research emerging AI methods, build supporting libraries and frameworks, implement cloud-based applications, and contribute throughout the software development lifecycle. Responsibilities include prompt engineering, model fine-tuning, vector database integration, code reviews, testing, deployment, maintenance, and cross-functional collaboration.

Build customer-facing AI prototypes, integrations, demonstrations, and proof-of-concept solutions using Python, Node.js, TypeScript, APIs, enterprise platforms, conversational AI, agents, and RAG. Translate requirements into technical implementations, connect Webex AI with CRM and contact-center systems, test reliability and security, measure outcomes, troubleshoot issues, and document reusable implementation patterns. Collaborate with customers, engineers, product teams, partners, and account teams to prepare solutions for production deployment. Travel approximately 15–25%.

Support customers through live chat by troubleshooting wireless internet products and services, resolving issues, and creating positive customer interactions. The role requires an energetic, customer-focused team leader who can engage customers effectively and contribute to strong client experiences.

Provides customer support through live chat, troubleshoots wireless internet products and services, and delivers positive customer engagement for clients. The role emphasizes meaningful interactions, energetic service, and resolving customer issues in a fast-paced environment.

Own and operate the AI security runtime control plane across Azure, AWS, and GCP. Build inline AI enforcement, tune prompt-injection, data protection, tool-call, and agent access policies, and integrate security platforms with SIEM/SOC workflows. Lead L3 investigations, automated containment, cloud and Kubernetes security, DevSecOps controls, vendor evaluations, and closed-loop red-team remediation. The role requires automation, infrastructure-as-code security, multi-cloud identity management, incident response, and collaboration with technical leadership.

Provides L2 monitoring, triage, investigation, and escalation for AI and cloud security detections. Analyzes telemetry using KQL or SQL, maintains dashboards, and produces operational, compliance, and risk reporting. Investigates AI threats including prompt injection, jailbreaks, data leakage, agent hijacking, and unauthorized tool activity. Monitors Azure, AWS, and GCP posture, supports vulnerability and compliance remediation, documents cases, and partners with SOC, cloud, incident response, and engineering teams.

Develop and deploy scalable AI and machine learning solutions using Generative AI, LLMs, RAG, NLP, and Computer Vision. Manage model lifecycles with Docker, Kubernetes, and MLOps practices. Optimize vector databases, prompt engineering, and model performance while conducting SQL-based data analysis and statistical modeling. Integrate AI capabilities into business workflows and maintain reliable, scalable deployment pipelines.

Leads customer-facing cloud security engagements across APAC, explaining and defending Genesys Cloud’s security, privacy, compliance, and AI-security posture. Responsibilities include security assessments, technical evidence development, architecture reviews, vulnerability and application-security discussions, contractual negotiations, technical workshops, executive briefings, and industry representation. Partners with Sales, Engineering, Product Security, Architecture, Privacy, Compliance, and AI teams while advising enterprise customers, auditors, partners, and executives.

Lead the design and delivery of AI context engineering and evaluation harness workstreams. Develop RAG, memory, token-management, ontology, MCP integration, sandboxing, orchestration, validation, and evaluation standards. Support pilot teams, define integrations with GitHub, Notion, and Slack, build YAML-based knowledge models and repo-native harness prototypes, and translate pilot evidence into company-wide best practices and technical standards.

Lead the design and implementation of agentic application-security workflows across the SDLC. Build AI agents for threat modeling, secure coding, CI/CD validation, testing, release risk assessment, and production feedback. Define security context models, guardrails, decision rules, human-review requirements, and escalation paths. Integrate workflows with engineering platforms, lead engineers across teams, validate outputs with security experts, and continuously improve agent quality, observability, and production readiness.

Lead technical consulting engagements across the software development lifecycle, including requirements gathering, estimation, architecture, implementation, testing, documentation, deployment, and support. Mentor developers, provide technical leadership, collaborate with solution architects and clients, translate designs into technical requirements, and deliver innovative software solutions using Java and AI/LLM technologies.

Leads enterprise AI/ML infrastructure strategy and architecture, building scalable MLOps platforms for thousands of production models and millions of predictions. Responsibilities include distributed inference, foundation model serving, generative AI pipelines, observability, drift detection, self-healing systems, Zero Trust security, governance, experimentation, and multi-cloud transformation. The role provides executive influence, technical mentorship, industry thought leadership, and oversight of advanced cloud-native infrastructure across global engineering teams.

Designs and delivers production AI/ML systems for enterprise clients, including generative AI, agentic AI, classical ML, data pipelines, and MLOps on cloud platforms. Leads engineers, owns architecture and production operations, runs client discovery, presents to executives, manages scope and risk, and ensures successful handoff. The role is hands-on, client-facing, and requires collaboration with U.S.-based teams on a shifted India schedule.

Architects and deploys production-grade agentic AI and multi-agent systems using LLMs, RAG, vector databases, prompt engineering, and NLP. Designs autonomous workflows, tool calling, memory, routing, evaluation, observability, and guardrails. Builds backend APIs and microservices while implementing testing for model drift, prompt regression, and agent reliability. Requires advanced Python, hands-on production Generative AI experience, and expertise with agent orchestration frameworks and vector search.

Freelance AI Specialist responsible for completing and maintaining a shared business knowledge base, improving AI workflows with Claude and Gemini, creating reusable prompts, connecting AI to business tools, documenting processes, training teams, and reporting progress. The role requires careful fact-checking, strong written communication, confidentiality judgment, basic scripting or automation skills, and self-directed project execution.

Define the AI-native phone’s hardware platform strategy, roadmap, and specifications. Evaluate mobile SoCs and components, including CPU, GPU, NPU, memory, connectivity, power, thermal, and battery capabilities. Translate product and AI requirements into hardware decisions, assess platform customization needs, collaborate with ODMs, OEMs, and chipset vendors, review architecture proposals, and establish validation criteria for current and future on-device AI workloads.

Evaluate, benchmark, deploy, and optimize AI models for on-device execution on Android hardware. Responsibilities include integrating models into applications and system services, reducing latency, memory use, and power consumption, applying quantization and compression, leveraging CPU, GPU, and NPU acceleration, and building hybrid local/cloud inference routing with agent systems.

Define the product vision and user experience for an AI-native phone. Identify high-value workflows, design proactive interactions across the operating system and applications, translate concepts into engineering requirements, prioritize prototype and launch capabilities, evaluate technical feasibility with Android and AI teams, and validate interaction patterns through user research.

Integrate AOSP or custom ROMs with smartphone hardware, including BSPs, vendor HALs, firmware, drivers, boot processes, cameras, sensors, modems, Wi-Fi, and Bluetooth. Troubleshoot hardware and firmware compatibility, collaborate with OEMs, ODMs, and chipset vendors, and optimize device power consumption and performance.

Build agent capabilities for AI phones, including workflow orchestration, tool integrations, context retrieval, memory, multi-step execution, long-running stateful workflows, and reliability improvements. Integrate local and cloud model providers, develop evaluation and regression tests, and support production AI infrastructure. The role requires backend or AI application engineering expertise, programming proficiency, LLM API and tool-calling experience, and knowledge of APIs, distributed systems, state management, and asynchronous execution.

Build integrations between AI agents and first-party applications, exposing structured actions and information retrieval interfaces. Develop cross-application workflows, reusable SDKs, and reliable asynchronous APIs supporting search, creation, updates, sending, and deletion. Manage authentication, authorization, and permissions; investigate integration limitations; and collaborate with Android engineers on system-level application access.

Develop and modify Android system services, privileged APIs, and AI integration interfaces at the OS level. Build reliable agent-to-system execution capabilities, manage permissions, IPC, application lifecycle, and background execution, and optimize system performance and resource consumption. Collaborate with AI agent engineers while debugging and improving Android platform functionality.