Top Artificial Intelligence Jobs in Chennai

Partner with stakeholders to identify and prioritize AI use cases, map current and future business processes, assess feasibility and value, recommend build/buy options, and ensure solutions meet architecture, data, security, governance, and responsible AI requirements. Act as a bridge between business, IT, data science, and delivery teams.

Build production AI-powered automation, data pipelines, analytics, alerting, and anomaly detection systems. Integrate LLM features with internal tools, develop cloud and platform services, and collaborate with stakeholders to automate manual processes. Engineers specialize initially in AI, data, or platform engineering while contributing across tracks through pairing, code review, and shared delivery. The role requires strong API and data-handling skills, production engineering experience, clear communication, and willingness to work from the Chennai office during US Eastern overlap hours.

Lead the architecture and delivery of an AI-assisted workflow migration platform. Design AI agents, LLM integrations, RAG and classification pipelines, Azure cloud services, parsing and conversion engines, evaluation harnesses, and automated tests. Define AI strategy, fidelity metrics, security and rollback approaches, and data requirements. Manage client escalations, stakeholder delivery, and a 3–8 person engineering team while converting Nintex workflow definitions into Power Automate logic.

Drive enterprise sales growth for Akamai Cloud Infrastructure Services by developing new accounts, building strategic account plans, advising executive stakeholders, and identifying cloud, compute, and AI workload opportunities. Lead consultative cross-selling and upselling, manage complex infrastructure deals, forecast pipelines, negotiate contracts, and coordinate enterprise sales teams and technical specialists through close and adoption.

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

Support and enhance ADI's global compliance program aligned to enterprise risk and ISO 37301. Monitor regulatory developments, update policies, execute risk‑based monitoring and testing, manage investigations and remediation, support audits, develop training and communications, track metrics, and collaborate with Legal, HR, Finance, IT, and Operations to drive continuous improvement.

Lead and support execution of ADI's global compliance programs: risk‑based monitoring, policy management, testing, investigations, training, ISO 37301 alignment, audits, remediation, and cross‑functional stakeholder coordination.

Develop and integrate scalable SAP BTP solutions using CAP, RAP, APIs, SAP HANA Cloud, and enterprise integration patterns. Build AI-ready SAP services, including MCP servers, and connect AI solutions with SAP applications and enterprise data platforms. Lead client engagements, technical specifications, architecture, estimations, quality reviews, incident and change management, risk escalation, and status reporting. Mentor junior team members, collaborate with SAP functional consultants, and ensure secure, reusable solutions aligned with clean core principles and service-level agreements.

The AI Engineer is responsible for designing, validating, and supporting engineering processes, ensuring compliance with development plans and customer requirements.

Sell and manage solution-level offerings in industrial automation, digital twin, AI and cybersecurity. Build customer relationships, promote Yokogawa technologies across industries, and support sustainable, technical solution adoption.

Lead enterprise architecture for NIQ’s cloud and on-premises platforms, with a focus on production-grade AI, Generative AI, and agentic AI solutions. Define reference architectures, secure agent patterns, governance standards, evaluation criteria, and technology roadmaps. Partner with engineering, product, security, privacy, legal, and data governance teams to deliver scalable, observable, compliant systems. Assess platforms and emerging technologies, lead proofs of concept and migrations, and provide architecture governance, coaching, and technical leadership.

Build AI-powered business solutions using Microsoft Copilot, Copilot Studio, Power Apps, Power Automate, SharePoint, AI Builder, and Dataverse. Develop Copilot agents, automated workflows, prompt libraries, and reusable AI accelerators; support governance, security, responsible AI, and enterprise adoption. Evaluate emerging technologies, create prototypes, deliver training and workshops, and collaborate with business and technology stakeholders.

Design, build, deploy, and support enterprise AI assistants, agents, copilots, RAG solutions, and workflow automation. Develop secure integrations, AWS cloud infrastructure with Terraform, containerized and serverless applications, and CI/CD pipelines. Monitor production platforms, improve reliability, and implement security, data governance, auditability, and Responsible AI controls. Collaborate with product owners, architects, and business stakeholders to scale solutions from MVP to enterprise adoption.

Evaluate AI-generated technical content, craft and answer advanced ML/AI prompts, assess model responses for accuracy, relevance, and clarity, identify errors or hallucinations, and provide feedback to improve AI systems.

Build production-grade AI systems by developing scalable APIs, integrating LLMs, contributing to RAG pipelines and agentic workflows, and writing clean Python. Responsibilities include testing, documentation, deployment support, optimization, collaboration, and continuous learning, with ownership increasing based on experience.

Supports the creation, publishing, translation, editing, and quality assurance of AI-generated educational content. Uses generative AI and multimedia tools to produce learning experiences, refine text, images, audio, and video, and publish materials in an LMS. Conducts manual accuracy and quality reviews, applies instructional design principles, explores efficiency improvements, monitors emerging AI trends, and provides feedback to improve internal content development tools.

Design, build, and operate ETL/ELT data pipelines, data models, REST APIs, and production AI/LLM applications. Develop AI skills, agent workflows, and MCP server integrations for internal systems and business use cases. Collaborate with product, data, and engineering teams while owning reliability, quality, documentation, and deployment. The role requires independent execution, strong communication, and experience with Python, SQL, data engineering, AI application development, and platform capabilities.

Leads OneMagnify’s AI practice by guiding client strategy, directing generative AI architecture and delivery, and translating business needs into production-ready solutions. Manages and mentors data scientists, AI engineers, and software engineers while overseeing Databricks-based data pipelines, model deployment, MLOps, and LLM applications. Partners across analytics, strategy, engineering, creative, and operations teams, presents technical concepts to executives, and contributes to evolving the company’s AI service offerings.

Build AI systems and solutions using machine learning, deep learning, and large language models. Prototype models with Python and common ML libraries, develop deep learning architectures, fine-tune transformer models, and implement retrieval-augmented generation using vector databases. Apply feature engineering, model selection, optimization, and validation techniques while working with SQL, data pipelines, Git, Jupyter, and Docker. Communicate technical trade-offs clearly and own experimentation and problem-solving efforts.

Design and implement scalable end-to-end AI/ML solutions and data pipelines; define secure cloud and on-prem architectures; lead MLOps, CI/CD, model lifecycle management, data governance, and cross-functional collaboration to drive AI initiatives in manufacturing.

Design, productionize, optimize, and deploy large-scale AI and machine learning models and infrastructure. Build distributed training and inference systems, resilient data pipelines, evaluation suites, monitoring, logging, and ML CI/CD workflows. Collaborate with research, data, and platform engineers to turn prototypes into scalable, low-latency applications. Provide technical leadership through code reviews, mentoring, and engineering best practices.

Design, develop, and optimize AI, SLM/LLM, and agentic solutions. Implement and tune algorithms and large-scale distributed training, orchestrate model fine-tuning and serving, analyze performance, define APIs, and communicate technical outcomes to non-technical stakeholders.

Leads AGL’s enterprise Data and AI architecture strategy across platforms, analytics, AI/ML, GenAI, data products, and integration services. Establishes architecture standards, governance frameworks, roadmaps, and responsible AI controls; guides investment decisions; strengthens data quality, privacy, security, lineage, and model governance. Partners with executives and technical teams, coaches architects and engineers, and evaluates emerging technologies to deliver scalable, reusable, and business-aligned capabilities.

Lead the architecture and development of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic AI systems for intelligent networking. Drive the full software lifecycle from design through production operations, establish technical direction, mentor engineers, and improve system reliability, security, observability, performance, and scalability.

Lead the technical vision, architecture, development, testing, deployment, and operations of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Build secure, resilient, observable services processing high-volume network data; guide technical strategy, improve engineering practices, and mentor engineers. Translate complex technical challenges into production-ready solutions across intelligent networking, cloud platforms, analytics, and automation.