Top Machine Learning Jobs in India

Supports sales-force effectiveness through data cleaning, access governance, KPI reporting, performance trend analysis, and migration from Excel tracking to Power BI dashboards. The role also provides user support, onboarding and training assistance, documentation, process standardization, and continuous improvement across regional sales entities.

Lead Engineer responsible for developing end-to-end machine learning, generative AI, conversational AI, RAG, business intelligence, and analytics solutions. The role covers experimentation, evaluation, monitoring, deployment, semantic modeling, unstructured document processing, cloud AI platforms, and enterprise AI governance. It requires applying advanced NLP and GenAI techniques to insurance use cases while communicating technical findings, risks, and business outcomes to technical and non-technical stakeholders.

Leads delivery of enterprise conversational AI platforms, including chatbots and voice bots. Oversees Agile execution, technical architecture, cloud-native engineering, CI/CD, Terraform, Responsible AI, and AI adoption. Partners with product, security, architecture, and business teams while managing risks and executive communication. Mentors engineers and technical leads, supports hiring and performance management, and ensures scalable, secure, compliant, and operationally excellent solutions.

Lead statistical activities for Phase I–IV clinical trials from protocol design through results reporting. Provide biostatistical expertise for clinical development plans, protocols, analyses, safety reporting, regulatory inquiries, and publications. Collaborate with cross-functional teams, oversee internal programmers and CRO vendors, use SAS and R for statistical analyses and quality control, apply machine learning methods, and mentor junior statisticians.

Develops and deploys production-ready AI applications using generative AI, cloud or on-premises infrastructure, deep learning, neural networks, chatbots, and image processing. Leads AI model implementation, coordinates cross-functional integration, makes team decisions, mentors engineers, and serves as a subject matter expert across multiple teams.

Leads the design, development, and deployment of production-ready AI and machine learning applications using LLMs, generative AI, cloud services, and on-premises pipelines. Responsibilities include developing scalable solutions, integrating deep learning and neural network capabilities, coordinating cross-functional teams, making technical decisions, and mentoring engineers.

Leads the design, development, and operationalization of machine learning, generative AI, prediction, and optimization solutions. Sets technical direction, mentors engineers, owns projects through the software development lifecycle, and contributes to MLOps and AI platforms. Collaborates with stakeholders, product owners, and technology teams to deliver scalable, data-driven capabilities using cloud, data engineering, and modern machine learning technologies.

Build and operate scalable Java backend services and APIs supporting AI-powered and traditional product capabilities. Integrate LLMs, agents, and retrieval systems into production applications while improving evaluation, monitoring, observability, CI/CD, security, reliability, and performance. The role requires collaboration with AI Engineers, Product Owners, and Cloud Operations teams in a hybrid Bangalore environment.

Serve as a technical expert throughout enterprise sales cycles, partnering with account executives and customers to identify requirements, demonstrate SingleStore solutions, develop and deploy proofs of concept, lead technical evaluations, tune queries, conduct load testing, and support account expansion and channel partnerships. The role requires expertise in distributed databases, data platforms, AI and ML, cloud and containerized environments, programming, and enterprise technology sales.

Leads the design, development, deployment, and optimization of AI, generative AI, and large language model solutions. Builds production-ready systems in Python, conducts ML experiments, integrates GenAI into platforms, and advances MLOps practices. Collaborates with product, engineering, and business teams, ensures responsible AI governance and regulatory compliance, communicates technical findings, and mentors AI engineers and scientists.

Lead design and delivery of agentic AI and LLM orchestration solutions: define reference architectures, build reusable GenAI/RAG components, operationalize agentic workflows, ensure security, observability, and cross-team adoption.

Develops production-ready AI and machine learning applications using cloud AI services, generative AI, large language models, deep learning, NLP, chatbots, and image processing. Responsibilities include leading teams, making technical decisions, mentoring, collaborating across teams, designing scalable solutions, and improving processes. Requires expertise in LLMs, cloud AI integration, TensorFlow or PyTorch, NLP, and at least five years of experience.

Designs and deploys production-ready AI and machine learning applications using generative AI, cloud or on-premises pipelines, deep learning, neural networks, chatbots, and image processing. The role involves improving system performance and reliability, documenting technical solutions, collaborating with teams, contributing to technical problem-solving, and mentoring junior team members.

Develop and deploy production-ready AI applications and pipelines using generative AI, cloud services, and on-premises infrastructure. Apply deep learning, neural networks, conversational AI, natural language processing, and image processing. Lead technical decisions, coordinate cross-functional teams, optimize system performance, and mentor junior engineers while ensuring scalable and reliable AI solutions.

Design and develop production-ready AI and machine learning applications using cloud AI services, large language models, and generative AI. Build scalable application pipelines, integrate deep learning and neural network solutions, and support chatbot and image-processing use cases. Responsibilities include researching emerging AI technologies, collaborating with stakeholders, testing and validating solutions, contributing to technical problem-solving, and mentoring junior team members.

Develops and governs AI capabilities for Finance by identifying use cases, designing automation and AI-enabled workflows, validating data and models, integrating AI with analytics platforms, and ensuring controls, compliance, monitoring, and human oversight. The role bridges Finance and IT, supports transformation programs, creates prompts and user enablement materials, manages stakeholders, and coordinates project governance. It requires finance process expertise, machine learning knowledge, analytics proficiency, project management skills, and willingness to travel.

Lead and grow a team of applied scientists developing scalable AI/ML applications. Drive research and product roadmaps, customer-facing delivery, technical architecture, and full machine learning lifecycle execution. Partner with product, engineering, business, and customer success teams to align priorities and deliver measurable business impact. Oversee advanced deep learning techniques, cloud-native solutions, quality standards, mentoring, and cross-functional collaboration.

Build and deploy production-grade machine learning, deep learning, and Generative AI solutions. Responsibilities include developing models, RAG pipelines, AI agents, prompt engineering, data and training pipelines, scalable APIs, and MLOps practices. The role evaluates and optimizes model performance, cost, and latency while collaborating with product, data, and engineering teams to move AI capabilities from proof of concept to production.

Leads the design, governance, and operation of enterprise MLOps frameworks for secure, scalable AI and generative AI deployment. Responsibilities include managing an MLOps engineering team, architecting AWS platforms, implementing automated ML pipelines, establishing data and model governance, monitoring model performance and drift, and ensuring security, reliability, compliance, and cost optimization. The role also requires strategic planning, stakeholder communication, and collaboration across data science, engineering, cloud, and security teams.

The IT GRC Risk and Automation Analyst manages enterprise GRC platforms, integrated risk tools, automated reporting, API integrations, evidence-collection workflows, and continuous compliance monitoring. The role conducts third-party security assessments and translates technical risks into business impacts. It requires expertise in security, privacy, AI governance, and risk frameworks, including NIST CSF, ISO 27001, SOC 2, GDPR, NIST AI RMF, ISO 42001, and the EU AI Act.

Build and deploy classical machine learning and GenAI solutions, including forecasting, recommendations, RAG, LLM applications, and agentic workflows. Prepare and validate large-scale datasets, evaluate models, monitor production systems, and optimize reliability and cost. Create dashboards and translate technical findings into actionable recommendations for customers and stakeholders. Collaborate with product, engineering, and business teams throughout the solution lifecycle.

Design and deliver scalable Generative AI solutions using LLMs, RAG, AI agents, vector databases, and multimodal models. Lead cloud, MLOps, security, governance, and architecture strategies across AWS, Azure, and Google Cloud. Translate business needs into technical solutions, support client workshops and pre-sales, present strategies to executives, and mentor data science, ML engineering, and software teams.

Own production machine learning environments and MLOps infrastructure. Design and maintain pipelines for data ingestion, feature engineering, model training, deployment, and monitoring. Productionize ML and GenAI services, manage feature stores and search infrastructure, automate CI/CD, and implement observability, security, patching, and cost optimization. Collaborate with development, QA, operations, and data science teams while participating in 24x7 on-call support.

Designs and maintains secure CI/CD pipelines for conventional software and ML models in regulated aerospace environments. Responsibilities include configuration baselining, release management, software quality assurance, lifecycle audits, traceability, corrective-action investigations, change-control governance, automated testing, and compliance with DO-178C, DO-254, and ARP4754B. The role also supports containerized deployments, safety-critical releases, and testing on physical target hardware.

Develop and deploy machine learning models and analytical solutions for semiconductor design, process optimization, device modeling, verification, manufacturing yield, testing, and reliability. Apply mathematical modeling, optimization, statistical inference, and physics-informed methods to improve semiconductor development and product quality. Drive adoption of data science methodologies across engineering and manufacturing teams, collaborate cross-functionally, mentor junior staff, and potentially publish or patent novel work.