Top Artificial Intelligence Jobs in Chennai

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 and implement production-grade AI models and algorithms, lead AI initiatives across organizations, enhance Agentic AI capabilities, and collaborate with teams to develop high-impact AI features.

The MDM Specialist will design and implement master data management solutions, integrate AI workflows, and ensure data governance and quality across customer and vendor domains.

Build end-to-end infrastructure for AI agents: design React/TypeScript UIs, implement Python agent harnesses, develop Kubeflow-based MLOps pipelines, fine-tune foundation LLMs (LoRA/QLoRA/SFT/RLHF), deploy and operate services on Kubernetes, integrate agent frameworks, and apply software engineering best practices, testing, and CI/CD.

Design and implement production-grade AI agent harnesses, build and operate MLOps pipelines with Kubeflow, fine-tune foundation LLMs (LoRA/QLoRA, SFT, RLHF), deploy and manage services on Kubernetes, integrate agent frameworks, and apply software engineering best practices to move models from prototype to production.

Develop and maintain AI systems for a financial platform, focusing on LLM pipelines, ML model serving, backend integration, and collaboration with engineering teams.

The Senior .Net Developer will enhance SDG's C#/.NET Core codebase, design high-load services, and optimize MS SQL queries, benefiting from a remote work environment and attractive benefits.

Assist in developing AI solutions for financial planning, working on projects involving data structures, algorithms, and integrating AI features into the platform.

Lead AI initiatives, craft product vision, manage lifecycle, utilize data for decisions, and foster collaboration across teams.

Lead customer-facing SaaS implementations and onboarding, design and deliver custom REST API integrations, troubleshoot complex technical issues, communicate clearly with stakeholders, and drive long-term customer success.

The AI Implementation Manager is responsible for managing customer onboarding, implementing solutions, and ensuring customer satisfaction in B2B environments, with a focus on technical integrations and strong communication with clients.

Build and deploy autonomous AI agents and internal copilots that research, enrich, reason, and execute GTM workflows. Implement RAG with embeddings and vector DBs, integrate agents with HubSpot and internal systems, automate data pipelines and scraping, and own agent systems end-to-end from prototype to production.

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.