Top Machine Learning Jobs in India

Lead plant digital transformation by running pilot projects, evaluating disruptive technologies, partnering with startups, implementing MES/IIoT, and owning end-to-end product management and adoption to improve efficiency, quality, and sustainability.

Support and troubleshoot critical applications and AI/ML solutions, including deploying, monitoring, and managing machine learning models on Google Cloud Vertex AI. Serve as a subject matter expert, analyze system issues, implement solutions, document technical processes, collaborate cross-functionally, and adopt emerging AI technologies.

Develop and optimize Deep Packet Inspection solutions for 4G/5G systems. Responsibilities include creating traffic signatures, analyzing application flows and network packets, working with Wireshark and PCAP files, building traffic simulation and automation tools, troubleshooting traffic issues, and researching DPI engine improvements. The role requires strong C programming, operating systems and networking knowledge, with Python, reverse engineering, and ML-based traffic classification as advantages.

Develop machine learning and deep learning models for engineering and automotive applications. Build predictive and surrogate models that reduce simulation effort, apply physics-informed machine learning, select appropriate deep learning architectures, and create AI-driven optimization and design-of-experiments solutions. The role also involves identifying engineering problems suitable for AI/ML and predictive analytics, with preferred experience in CAE, automotive simulation, and physics-based modeling.

Develop and deploy machine learning and statistical models for business and financial use cases. Analyze large datasets to identify trends, patterns, and actionable insights. Perform data cleaning, preprocessing, feature engineering, validation, evaluation, and optimization. Collaborate with business and technical teams to deliver scalable AI and machine learning solutions, including predictive models and data-driven applications.

Develop and implement machine learning and deep learning models for engineering and automotive applications. Build predictive, surrogate, physics-informed, and physics-guided models; apply AI-driven optimization and design of experiments; and use advanced architectures such as CNNs, LSTMs, Transformers, and GNNs. Experience with CAE, automotive simulation, or platforms such as LS-DYNA and ANSA is preferred.

Designs, develops, deploys, and optimizes machine learning and data science solutions. Responsibilities include analyzing complex datasets, building predictive models, feature engineering, model evaluation, validation, data preprocessing, and managing end-to-end data science projects through production deployment. The role also involves collaborating with business and technical stakeholders, improving ML models and data pipelines, and communicating technical insights effectively.

Lead the design and delivery of AI/ML and Generative AI solutions, including RAG systems, LLM applications, prompt-driven agents, automation pipelines, and proof-of-concepts. Develop and fine-tune models, evaluate AI frameworks and cloud services, establish responsible AI practices, guide technical best practices, collaborate with stakeholders, and ensure enterprise security and compliance.

Develop and enhance ServiceNow CMDB, ITOM, and ITSM solutions with AI-driven automation, AIOps, GenAI, intelligent incident routing, and predictive insights. Build workflows, integrations, applications, and automation using JavaScript, Glide scripting, REST APIs, Flow Designer, Business Rules, and Script Includes. Configure Discovery and Event Management, translate business requirements into scalable solutions, collaborate with stakeholders, and optimize existing ServiceNow applications while following security, governance, performance, and scalability standards.

Leads the design, development, deployment, and optimization of scalable AI/ML solutions, including generative AI, LLM, NLP, and RAG applications. Drives architecture and solution design, develops production-ready models, collaborates with engineering and R&D teams on proofs of concept and deployments, ensures application scalability and reliability, and mentors engineers.

Develop machine learning and data science solutions, analyze complex datasets, build and evaluate predictive models, perform preprocessing and feature engineering, and communicate insights to stakeholders. The role also involves translating business needs into data-driven solutions, monitoring model performance, and improving models based on business requirements.

Develop and optimize machine learning and deep learning models, perform data preprocessing and feature engineering, and deploy models using MLOps practices. The role involves Python-based development with Scikit-learn, TensorFlow, or PyTorch, cloud platforms such as AWS, Azure, or GCP, and potentially NLP or computer vision projects.

Develop and implement AI and generative AI solutions using Python, machine learning, deep learning, LLMs, prompt engineering, RAG, and frameworks such as LangChain or LlamaIndex. Integrate APIs and deploy solutions on AWS, Azure, or GCP. The role is open to candidates with 1–10 years of experience and favors immediate joiners across India.

Experienced data scientists will work on data, analytics, and AI/ML projects across India. Responsibilities involve data analysis, visualization, predictive modeling, feature engineering, machine learning model development and evaluation, and applying statistical methods. Required tools include Python, SQL, Pandas, NumPy, and Scikit-learn. NLP, deep learning, and cloud platforms such as AWS, Azure, or GCP are advantageous. Immediate joining is preferred.

Capture natural human movements and everyday activities using company-provided VR equipment to support AI model and robotics training. Follow detailed instructions, perform and record production activities for 4–8 hours daily, maintain strong observation and attention to detail, and collaborate effectively in a production-based environment.

Develop, deploy, optimize, and continuously improve AI/ML models and solutions using Python. Manage end-to-end model development and deployment, analyze complex problems, collaborate with cross-functional teams, and apply AI security and risk-management best practices.

Develop and deploy AI applications using Python, machine learning, OpenAI technologies, FastAPI, REST APIs, Azure, AutoGen, and MCP. Build and integrate machine learning models, containerize applications with Docker and Kubernetes, implement CI/CD pipelines, and support scalable AI application deployment.

Build and deploy AI and machine learning models for business challenges, develop Generative AI and Agentic AI solutions using LangChain and LangGraph, leverage LLMs, create data visualizations and actionable insights, and collaborate with cross-functional teams while managing data science projects.

Develop Generative AI solutions using Python and FastAPI. Build and integrate machine learning, deep learning, NLP, LLM, prompt engineering, and RAG systems. Work with text embeddings and vector databases including FAISS, Chroma, and Pinecone. Implement model inference, deployment, REST APIs, and backend integrations. The role is hybrid in Hyderabad or Bangalore and requires candidates with 4.5+ years of relevant experience who are currently serving notice and can join within 15 days.

Leads the development of scalable, production-ready AI solutions using machine learning, generative AI, NLP, deep learning, neural networks, and cloud AI platforms. The role requires Python, TensorFlow, PyTorch or Keras, prompt engineering, Hugging Face, spaCy or NLTK, and data visualization tools. It also involves applying statistical modeling, working with Agile/Scrum methodologies, and collaborating with stakeholders.

Lead machine learning engineering and MLOps initiatives, including ML lifecycle management, automated pipelines, Docker and FastAPI model deployment, cloud and multi-cloud AI platforms, CI/CD, model registries, batch and real-time inference, monitoring, drift detection, and performance optimization. The role requires strong Python and machine learning framework expertise along with analytical, troubleshooting, and stakeholder management skills.

Build and own production-grade machine learning and generative AI systems, including LLM applications, RAG pipelines, conversational AI, agentic workflows, personalization, recommendations, and retrieval systems. Develop scalable APIs and backend services, evaluate and optimize AI systems for quality, latency, cost, and reliability, and manage solutions through experimentation, deployment, monitoring, and continuous improvement.

Source, screen, assess, and place technical IT candidates for international clients. Build talent pipelines through LinkedIn Recruiter, social networks, referrals, talent communities, and headhunting. Manage client requirements, interviews, feedback, offers, onboarding, candidate relationships, recruitment metrics, and records while supporting or leading end-to-end hiring across global markets.

Lead product vision, strategy, roadmap, and lifecycle delivery for AI-first SaaS loyalty platform capabilities. Translate customer and market insights into priorities, requirements, launches, adoption plans, and measurable outcomes. Collaborate with Engineering, Design, AI/ML, QA, Sales, Marketing, and Customer Success, while creating product documentation and mentoring Product Managers. Drive AI-powered features using LLMs, agents, automation, or machine learning in production environments.

Leads the engineering organization and oversees software delivery, system architecture, platform security, AI adoption, and engineering excellence. Scales high-traffic e-commerce platforms, integrates AI technologies into products and development workflows, ensures compliance, and partners with Product, Operations, founders, and senior leadership. The role also includes team development, mentoring, cross-functional collaboration, and ownership of the engineering and AI roadmaps.