Top Artificial Intelligence Jobs in Chennai

Join Welo Data’s Nepali contributor network for flexible, remote AI data projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, or chatbot responses; create prompts for large language models; and review cultural and linguistic accuracy. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Join Welo Data’s Cantonese AI Trainers Network to support AI data annotation, evaluation, and prompt creation. Work remotely with flexible hours while contributing language and cultural expertise to improve AI systems. Applicants should be native or near-native Cantonese speakers living in Hong Kong, Macau, mainland China, or Malaysia, with strong English skills, digital tool proficiency, attention to detail, and curiosity.

Join Welo Data’s contributor network for flexible, remote AI projects involving text, image, or audio annotation; search, ad, and chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Candidates need native or near-native Kannada fluency, English proficiency, digital tool comfort, and strong attention to detail. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Join Welo Data’s Vietnamese AI contributor network for potential remote projects involving text, image, or audio annotation; search, advertising, and chatbot evaluation; prompt creation; and cultural and linguistic quality review. This is not an active opening. Projects vary in duration and compensation, with details provided before participation.

Join a remote global contributor network supporting AI data projects. Depending on project needs, you may annotate content, evaluate search results or chatbot responses, create prompts, and verify cultural and linguistic accuracy. Projects range from a few days to several weeks, with scope, timelines, and compensation provided before participation.

Own the technical delivery of AI solutions from stakeholder engagement and conceptualization through proof of concept, productionization, and maturity. Design multi-agent, generative AI, RAG, and fine-tuned LLM solutions; develop evaluation and AgentOps practices; and apply NLP, computer vision, and predictive models. Support pre-sales, create technical artifacts, guide engineering teams, and deploy scalable solutions using microservices, containers, Kubernetes, AWS, and Azure.

Leads the development and maintenance of scalable test automation frameworks using Playwright and Selenium. Develops Java and Python automation scripts, integrates test suites with Azure DevOps and Jenkins pipelines, and implements AI-assisted capabilities such as script generation, self-healing, intelligent test data, and failure analysis. Mentors automation engineers, conducts code reviews, collaborates in Agile teams, and promotes quality engineering best practices.

Develop and optimize AI and generative AI applications using Python, large language models, machine learning, NLP, TensorFlow, and PyTorch. Build APIs, backend services, semantic search systems, and vector database integrations. Fine-tune models, monitor production performance, and collaborate with data scientists, engineers, and product teams to deliver scalable AI features. Document models and workflows while applying prompt engineering, embeddings, and MLOps practices.

Work directly with founders to identify business problems and build, test, deploy, and improve AI-driven automations, workflows, internal tools, and LLM or agent-based solutions across marketing, sales, and product. The intern will collaborate with teams to understand workflows, adapt existing AI tools, demonstrate solutions, and iterate based on feedback.

Leads the design, development, deployment, and operation of enterprise-scale AI, machine learning, NLP, GenAI, and agentic AI solutions. Builds scalable Python pipelines on Databricks and Azure, develops distributed ML applications, and implements MLOps, LLMOps, CI/CD, and responsible AI practices. Collaborates with technical and business teams to deliver actionable intelligence, establishes AI strategies and roadmaps, mentors junior engineers, and supports team scaling.

Build and scale production machine learning systems for ecommerce search, retrieval, ranking, recommendations, personalization, and GenAI experiences. Responsibilities include developing ML models, optimizing multi-stage ranking pipelines, running offline and A/B experiments, applying reinforcement learning and bandits, deploying Databricks pipelines through AKS, designing scalable ML infrastructure, and monitoring models using MLOps and LLMOps practices.

Designs, develops, evaluates, and deploys enterprise-scale AI, machine learning, NLP, generative AI, and agentic solutions for e-commerce. Builds scalable Python pipelines on Databricks and Azure, develops distributed streaming ML applications, and drives software delivery through DevOps, MLOps, and LLMOps practices. Collaborates cross-functionally to translate business needs into reliable AI products while implementing responsible AI, explainability, fairness, human validation, compliance, and governance.

Designs and scales AI/ML platforms for pricing, promotion optimization, and demand forecasting across B2C and B2B channels. Develops time-series, causal inference, reinforcement learning, recommendation, and price elasticity models. Builds end-to-end ML pipelines, production API integrations, monitoring, retraining, and drift detection. Collaborates with data engineering, product, marketing analytics, cloud, and business teams to optimize revenue, profitability, promotions, competitive pricing, and customer-segment decisions.

Architect and improve autonomous AI optimization algorithms for a marketing platform. Analyze A/B tests, integrate AI models, develop multi-touch attribution, expand advertising-channel capabilities, reduce inference costs and latency, incorporate customer feedback, and maintain reliable, continuously improving optimization loops.

Design and build scalable cloud-native data pipelines to ingest, clean, transform, chunk, embed, index, and serve enterprise data for LLM applications. Optimize vector database architectures, embedding generation, and RAG pipelines, collaborate with ML and software teams, implement Airflow orchestration, and ensure pipeline performance, monitoring, and reliability on AWS.

Design, develop, prototype, test, and deploy machine learning and AI models. Collaborate with data engineers and product stakeholders to build production AI pipelines and refine model performance. Maintain deployments and iterate on algorithms to enhance functionality.

Design and implement Generative AI solutions to improve operational efficiency. Collaborate with teams to identify opportunities and ensure data governance.

Design, develop, and deploy AI-powered automation for hardware performance verification. Build intelligent tools and agents for test generation, regression detection, failure analysis, and corrective recommendations. Integrate LLM assistants, create verification data pipelines, establish impact dashboards, and collaborate with verification engineers. Apply generative AI, reinforcement learning, and formal methods to improve coverage, reduce cycle times, and increase productivity across complex GPU, CPU, memory, and HPC verification workflows.

The Senior Engineer will develop and expand the AI Studio Platform, design innovative prototypes, collaborate on cloud-native solutions, and research emerging technologies in AI and automation to enhance business outcomes for Presidio's clients.

Leads architecture, development, deployment, and optimization of large-scale AI/ML, generative AI, and agentic systems. Oversees data pipelines, model training, monitoring, validation, and lifecycle practices while mentoring data science and engineering teams. Partners with product and business stakeholders, establishes secure and ethical AI standards, analyzes conversational data, evaluates emerging technologies, and provides technical thought leadership on scalable model architectures and MLOps.

Own end-to-end customer implementation operations, onboarding, recurring data deliveries, production workflows, and operational support. Support AI-enabled implementations, validate data quality, troubleshoot delivery workflows, collaborate with technical and customer-facing teams, and use GenAI and automation to improve efficiency, reliability, documentation, and scalability.

Design, build, and operate production-grade backend services using Node.js and TypeScript. Integrate LLMs and AI services, develop RESTful APIs and data layers, implement RAG pipelines with vector databases, and apply agentic workflows. Maintain automated testing with Jest and Playwright, deploy services on Azure or AWS, and support scalable systems for banking and fintech clients with strong reliability, security, and auditability requirements.

Leads the architecture, development, deployment, and operation of scalable AI infrastructure, distributed systems, real-time data platforms, microservices, and agentic AI solutions. Defines technical vision, builds resilient and observable production services, processes high-volume network data, mentors engineers, and drives engineering excellence across intelligent networking and automation platforms.

Design, develop, deploy, and scale enterprise AI and machine learning solutions, including predictive models, recommendation engines, automation, and model-serving APIs. Build data pipelines and MLOps workflows, monitor model performance and drift, troubleshoot production systems, and implement responsible AI practices. Collaborate with technical and business stakeholders, contribute to architecture and code reviews, and mentor junior engineers while applying cloud-native technologies.

Build, deploy, and manage end-to-end AI services for traditional and generative AI use cases. Design scalable AI/ML solutions, distributed computing systems, training and deployment pipelines, and advanced algorithms. Analyze and optimize model performance across cloud platforms, GPUs, containers, and serving platforms while collaborating with cross-functional teams to deliver business-focused solutions.