Top Machine Learning Jobs in India

Design, build, and deploy scalable AI/ML and generative AI solutions (including RAG and agentic workflows) for sales and distribution modernization. Integrate APIs and microservices with enterprise systems (CRM, enrollment, broker ecosystems), implement MLOps, monitor model performance, ensure Responsible AI and security compliance, collaborate with product, engineering, offshore teams, and drive high-quality execution.

Design, prototype, test, and deploy AI-powered solutions and intelligent agents. Integrate AI services with enterprise platforms, APIs, and workflows. Collaborate with architects and stakeholders to translate requirements, contribute reusable frameworks, and support transition of pilots to production.

Build, configure, test, and document data science, machine learning, advanced analytics, and generative AI solutions. Prepare data, develop features, prompts, retrieval components, prototypes, scripts, and technical artifacts. Execute model and GenAI evaluation plans, validate data, troubleshoot defects, maintain reproducible documentation, and follow governance, security, and Responsible AI controls. Collaborate with global AI practitioners while contributing reusable code, testing assets, and technical methods in a regulated financial-services environment.

Lead multiple R&D Dev and QA teams building cloud-native, enterprise SaaS products. Manage hiring, reviews, mentoring, and people operations; act as Scrum Master; own delivery, code quality, test automation, and unit testing; collaborate with Product and Architecture on planning; participate in release planning and product strategy; serve as engineering contact for defect management and support.

Lead and technically drive multiple teams to design, build, and operate large-scale distributed web and cloud services. Influence product vision, adopt AI/ML (including LLMs and agentic frameworks), ensure service health, deliver high-quality results, hire and mentor engineers, and collaborate across product and services groups.

Seeking a Vice President to develop Machine Learning models and Gen AI use cases in Wealth Management, supporting business lines with predictive analytics and operational model management.

Lead the AI strategy, architecture, development, and deployment of scalable AI-powered Metaverse products. Build LLM applications, agents, copilots, RAG systems, and transformer-based models while guiding model evaluation, governance, security, responsible AI, cloud deployment, and MLOps. Collaborate across product, engineering, UX, BIM, CAD/CAE, digital twin, and Metaverse teams. Mentor AI engineering and data science teams, manage stakeholders, and deliver enterprise-scale solutions.

Lead architecture and hands-on development of scalable, cloud-native full-stack applications using .NET Core, C#, React.js, and Azure DevOps. Design REST APIs, microservices, and distributed systems while driving scalability, security, performance, and reliability. Contribute to machine learning, digital twin, data pipeline, model deployment, and MLOps initiatives. Mentor engineers, review technical work, collaborate with stakeholders, and support delivery planning, estimation, and technical risk management.

Lead the architecture, design, and hands-on development of scalable full-stack cloud-native applications using .NET Core, C#, React.js, Azure, REST APIs, microservices, and distributed systems. Establish technical standards, review code, optimize performance and security, and mentor engineers. The role also supports AI/ML integration, Digital Twin architectures, data pipelines, model deployment, MLOps, stakeholder collaboration, delivery planning, and technical risk management.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

Enterprise Account Executive responsible for acquiring new enterprise customers and expanding platform adoption across assigned Indian accounts. Owns the full sales cycle, including prospecting, discovery, solution workshops, proof-of-concept scoping, business-case development, forecasting, negotiation, and closing. Builds relationships with executive and technical buying stakeholders, partners with solution engineers, maintains pipeline coverage, and contributes to territory planning and competitive intelligence. Sells data, analytics, AI/ML, BI, and cloud platform capabilities to large enterprises.

Manages a software engineering team developing and maintaining network security products. Partners with product management and cross-functional teams to deliver business priorities, improve quality and efficiency, and drive innovation. Leads hiring, mentoring, team development, and scaling investments. Requires deep wireless, telecom, VoIP, networking, distributed systems, software architecture, Java or C++, SS7/GTP protocols, and Linux routing expertise.

Own and grow Fortune 500 and large enterprise accounts through partner-led sales motions. Build account plans, engage C-level stakeholders, shape data and AI adoption strategies, lead complex platform deals, negotiate enterprise commercial structures, manage SI and cloud partner ecosystems, maintain pipeline and forecast accuracy, and coordinate post-sale deployment and expansion with customer success and professional services.

Engineering interns will work with developers and data scientists to build, test, and ship features for an industrial data platform. Responsibilities include developing backend APIs, microservices, and Angular dashboards; supporting machine learning models, industry templates, and GenAI test scripts; participating in code reviews, bug fixes, automated testing, and technical documentation.

Lead the development and scaling of AI and machine-learning products for predictive analytics and time-series modeling. Design predictive models, evaluate performance, communicate actionable findings, collaborate cross-functionally, monitor industry trends, and mentor junior data scientists. Apply statistical and machine-learning methods, signal-processing techniques, and product-engineering expertise to analyze complex sensor and IoT data.

Own enterprise pipeline generation for strategic accounts across BFSI, manufacturing, and government. Design account-based outbound campaigns, conduct consultative discovery with C-level buyers, maintain CRM data quality, track conversion metrics, and partner with Account Executives on account planning. Mentor junior BDRs, support playbook development, and engage prospects around Rubiscape’s unified data, BI, ML, and AI platform.

Develop and maintain Python applications, frameworks, APIs, automation scripts, and integrations. Analyze data using Pandas, NumPy, and Matplotlib; extract text with OCR; identify tool issues and implement AI-oriented solutions. Manage IT systems, assets, and infrastructure while writing tests, optimizing performance, documenting processes, and supporting cross-departmental integration.

Develop and maintain high-performance 3D modeling software using advanced geometry processing, rendering, simulation, and visualization techniques. Integrate 3D assets, graphics APIs, game engines, and emerging VR, AR, and AI technologies. Build testing frameworks, troubleshoot complex issues, document software, conduct code reviews, provide technical support, and mentor junior developers while collaborating with designers, artists, engineers, and QA teams.

Develop and test C++ software for high-performance embedded Linux systems and automotive infotainment head units. Responsibilities include optimizing applications for x86, ARM, NEON, and CUDA architectures; designing software frameworks, modules, requirements, and interfaces; managing integration, builds, and releases; documenting designs and tests; and flashing and testing applications on embedded hardware. The role also involves supplier coordination, requirements clarification, and collaboration within Agile teams.

Develop end-to-end data science and AI/ML solutions involving statistical modeling, machine learning, generative AI, LLMs, NLP, and unstructured data analytics. Build data models and algorithms, solve ambiguous business problems, collaborate with project managers and multiple teams, and deliver actionable insights using cloud platforms such as AWS, Azure, or GCP. Present findings and communicate effectively with technical and business stakeholders.

Develop and deploy generative AI applications, integrate LLM APIs, perform text analysis using traditional machine learning and GenAI, and manage the machine learning lifecycle from problem definition through deployment and monitoring. Collaborate with cross-functional teams, translate business needs into technical solutions, evaluate new GenAI techniques, and document technical concepts.

Leads end-to-end data science and AI projects, including modeling, testing, deployment, and value realization. Manages multidisciplinary teams, coordinates with Product, IT, Climate, Risk, GIS, and business stakeholders, and applies peril modeling, climate risk, insurance, and geospatial expertise. Maintains the DSAI operating model, establishes development best practices, and oversees team capability development, mentoring, and performance management.

Leads AI innovation across HR SaaS products by owning the AI software development lifecycle, designing agentic and RAG systems, building prototypes, defining production specifications, guiding engineers, and improving in-house AI platforms. Responsibilities include prompt and context engineering, evaluation, monitoring, model optimization, orchestration, governance, and mentoring teams while scaling reliable AI features.

Ensures the quality, reliability, and performance of AI products, model outputs, datasets, and supporting data pipelines. Responsibilities include designing AI/ML test strategies, evaluating prompts and LLM responses, detecting hallucinations and bias, validating ETL/ELT processes and dataset integrity, defining model quality metrics, automating API and data validation tests, integrating tests into CI/CD, and collaborating with engineering, data science, product, and QA teams.

Deliver and create online and offline data science training, mentor aspiring data professionals, develop accessible training content, solve business problems using machine learning, and promote data science skills within the learning community.