Top Machine Learning Jobs in India

Build and scale AI-enabled healthcare products spanning clinical decision support, anomaly detection, causal reasoning, natural language generation, agent infrastructure, and rapid prototypes. The role requires full-stack engineering fluency, strong product judgment, hands-on GenAI and agentic AI experience, and ownership from problem definition through production deployment. The advisor will partner with engineering, data, AI, clinical, and business stakeholders in a regulated, global environment.

Leads the design, development, deployment, and optimization of enterprise AI platforms, agents, multi-agent systems, LLM applications, and RAG solutions on Azure. Integrates AI with Microsoft enterprise platforms and APIs, builds production-grade workflows, and establishes observability, security, governance, evaluation, and responsible AI controls. Collaborates cross-functionally to prototype solutions and develop reusable architecture, prompting, deployment, and AI engineering standards.

Leads the architecture and operationalization of enterprise AI platforms. Establishes MLOps, LLMOps, model lifecycle management, observability, governance, Responsible AI controls, and reusable architecture standards. Collaborates across technical and business teams to deliver production-ready AI capabilities, evaluates emerging technologies, and maintains model governance and documentation. Requires advanced Python and PySpark skills, cloud AI platform experience, statistical modeling expertise, and hands-on KubeFlow, BentoML, Great Expectations, and Evidently AI experience.

Lead the design, development, and delivery of optimization solutions, simulation models, and AI/ML products for container terminal operations. Develop models for scheduling, routing, resource allocation, yard positioning, and vessel sequencing; evaluate solutions through simulation and operational data; collaborate with stakeholders to deploy production systems; and mentor junior team members.

Leads applied AI research by translating ambiguous business problems into experiments, proof-of-concepts, and evaluated solution architectures. Develops and compares AI approaches involving machine learning, deep learning, generative AI, LLMs, RAG, and agentic AI. Creates implementation-ready engineering handoffs, reusable research assets, evaluation methods, and reference architectures while collaborating with business, product, engineering, data, cloud, and architecture teams. Mentors junior team members and provides hands-on technical leadership.

Design, deploy, and maintain scalable machine learning systems on GCP. Build training and inference pipelines, automate CI/CD and model deployment with Vertex AI, manage model versioning and rollouts, monitor drift and performance, and support production troubleshooting. Collaborate with data scientists, engineers, and stakeholders to operationalize models, improve platform reliability, and document production-ready workflows.

Designs and embeds an enterprise process management framework, including governance, process hierarchies, documentation standards, approval workflows, quality controls, and scalable delivery models. The role supports process analysis, automation and AI readiness, enables distributed teams, develops playbooks, and transfers capability to the Enterprise PMO and business stakeholders. It requires strong enterprise thinking, stakeholder engagement, operational discipline, and experience building process capabilities from first principles across functions and geographies.

Leads the strategic vision, development, and deployment of AI/ML and Generative AI capabilities. Oversees technical teams, model scalability, MLOps, compliance, risk management, and multiple project deliveries. Partners with product, engineering, business development, clients, and other stakeholders to integrate AI solutions into low-code/no-code and pro-code platforms. Requires strong expertise in machine learning, data analysis, NLP, GenAI, cloud platforms, and technical leadership, preferably within banking or financial services.

Leads product marketing for IoT-based building automation and energy-efficiency solutions. Responsibilities include developing product content, messaging, campaigns, storytelling, sales enablement materials, budgets, and customer communications. The role gathers customer and competitive insights, conducts win-loss analysis, supports pricing and positioning, educates internal teams, manages vendors, and aligns product marketing across Sales, Engineering, Marketing, and Customer Success. Strong HVAC controls, BMS, cloud, analytical, communication, and collaboration expertise is required.

Lead the design, development, evaluation, and deployment of advanced GenAI, NLP, LLM, RAG, and agentic AI solutions for healthcare and life sciences. Responsibilities include architecting scalable pipelines, optimizing model performance, integrating vector search and cloud platforms, conducting experimentation, ensuring responsible AI and production reliability, collaborating with technical and domain teams, and mentoring ML engineers.

Design and deploy AI/ML solutions from proof of concept through production. Build traditional and deep learning models, LLM, generative AI, RAG, and agentic systems; develop end-to-end ML pipelines; evaluate model accuracy, bias, safety, and robustness; and support deployment, monitoring, governance, and compliance in regulated healthcare environments.

Designs, develops, evaluates, and deploys AI and machine learning solutions from proof of concept through production. Builds traditional ML, deep learning, LLM, generative AI, RAG, and agentic systems; develops end-to-end ML pipelines; evaluates model accuracy, bias, robustness, and safety; and supports cloud deployment, monitoring, governance, documentation, and compliance in regulated healthcare environments.

Own and improve the Command|Alert alerting platform, including rule evaluation, anomaly detection, event correlation, reliable Kafka and OpenSearch pipelines, and downstream notifications. Convert security and network telemetry into usable topologies and dependency maps, support LLM-based alert generation, resolve production issues, lead reliability improvements, deliver independent projects, and mentor engineers while shaping technical direction.

Lead enterprise AI and full-stack platform architecture across multiple initiatives. Design and ship resilient microservices and REST APIs, build RAG and multimodal LLM features, develop agentic workflows, and establish AI-assisted development standards. Provide technical leadership, mentor engineers, oversee contractors and vendors, and make end-to-end decisions balancing reliability, performance, scalability, and cloud cost.

Leads enterprise AI and full-stack platform architecture across multiple initiatives. Designs microservices and REST APIs, builds RAG, multimodal LLM, and agentic workflow features, and establishes standards for AI-assisted development. Oversees reliability, performance, and cloud costs while delivering reusable AI components. Mentors engineers and guides contractors and partners, with responsibility for technical decisions, architecture, code quality, and production-scale AI systems.

Design, build, and operate observability, anomaly detection, and reconciliation tooling for monetization systems. Implement metrics/logs/traces, automated billing and data anomaly detection, SLOs, runbooks, incident response, and collaborate with product, finance, and support. Leverage telemetry and machine learning to predict and resolve monetization issues, and guide technical direction for reliability and detection work.

Design, validate, and monitor card fraud strategies using data analysis and analytics/AI. Partner with product, technology, and operations to develop, test and implement detection and treatment capabilities, balancing fraud mitigation with revenue, costs, and customer impact.

Design and build production-grade AI applications embedding ML/LLM capabilities. Integrate trained models into APIs, services, UIs, and workflows. Operationalize models (versioning, deployment, monitoring), implement guardrails, ensure security/privacy/compliance, apply bias mitigation and explainability, and mentor team members on AI engineering best practices.

Own end-to-end revenue performance across Sales and Customer Success/Growth for the InSEA region. Lead acquisition, retention, expansion, forecasting, product penetration, customer advocacy, and regional marketing partnerships. Build and scale the commercial organization, establish disciplined operating processes, and collaborate with global Analytics, Machine Learning, Product, Marketing, and Operations leaders. The role requires deep Greater China mobile advertising expertise, senior customer relationships, and fluency in Mandarin and English.

Design, develop, and deploy AI/ML and LLM solutions (NLP, RAG, embeddings, vector DB) across sales and digital use cases. Perform data analysis, build model APIs, integrate diverse data sources, identify opportunities for AI adoption, and collaborate with cross-functional teams to improve user experience and automate processes.

Design, build, and deploy ML models and end-to-end pipelines (data ingestion, feature engineering, training, validation, serving). Implement MLOps practices, containerized deployments, model monitoring, and retraining workflows. Collaborate with cross-functional teams, optimize model performance for production, write production-quality code, participate in architecture decisions, and mentor junior engineers.

Designs, builds, migrates, and operates AWS workloads and platform infrastructure. Responsibilities include cloud architecture, infrastructure as code, security, cost optimization, environment provisioning, lifecycle management, DevOps and CI/CD, container platforms, and AI foundation enablement. The role uses Terraform, CloudFormation, AWS CDK, Python, Docker, Kubernetes, and numerous AWS services while applying the AWS Well-Architected Framework.

The Senior Software Engineer will develop AI agents, design data architectures, and ensure compliance with healthcare regulations while collaborating across teams.

Leads a multidisciplinary engineering team building Testsigma’s AI agent platform, knowledge graph infrastructure, and core product. Owns engineering delivery, software quality, platform reliability, SLOs, incident response, hiring, team development, product partnership, and customer-facing technical escalations. Collaborates with product and go-to-market teams to prioritize roadmap commitments, address reliability concerns, and ensure high-quality releases.

Creates accessible data science content on machine learning and deep learning, researches state-of-the-art techniques from open-source projects and journals, and develops and supports MOOCs covering data science, computer vision, machine learning, and NLP. The role requires Python, machine learning, deep learning, NLP, strong communication skills, and a six-month availability.