Top Machine Learning Jobs in India

Develop advanced analytics, AI/ML models, data pipelines, dashboards, and reporting solutions for Identity and Access Management controls. Analyze identity and entitlement data to detect anomalies, toxic access, control gaps, and IAM risks. The role also embeds model governance, AI risk management, explainability, monitoring, audit traceability, and responsible AI practices while partnering with security, risk, audit, compliance, privacy, and technology stakeholders.

Analyzes credit, bureau, transactional, and repayment data to identify risk drivers and improve lending decisions. Builds credit risk features and predictive model inputs, supports policy experimentation, monitors model and feature performance, and conducts portfolio analytics such as vintage, roll-rate, collections, and loss-driver analysis. Collaborates with engineering and data teams on quality, lineage, reproducible pipelines, governance, and production-ready documentation.

Design, build, and scale enterprise AI-powered platforms using GenAI, LLMs, RAG, embeddings, and NLP pipelines. Architect distributed, microservices-based, event-driven systems and production-grade APIs using Go or Python, cloud platforms, databases, and containerized deployments. Lead system design, secure engineering, CI/CD, observability, reliability, and troubleshooting. Influence technical direction across teams, mentor engineers, collaborate with stakeholders, and drive end-to-end delivery of scalable AI solutions.

Designs and delivers enterprise AI and machine learning solutions, including generative AI, fine-tuned LLMs, RAG, agentic systems, Document AI, OCR, and model deployment. Partners directly with business stakeholders to gather requirements, architect solutions, and own end-to-end delivery. Applies supervised, unsupervised, reinforcement learning, deep learning, and NLP techniques using Python, PyTorch, cloud platforms, APIs, and containerized environments. Healthcare experience is preferred.

Lead international expansion of AI-enabled agricultural weather forecasting programs. Responsibilities include selecting new markets, establishing offices and teams, developing government and telco partnerships, overseeing forecast deployment, managing staff and consultants, and building replicable best practices. The role reports to the VP of Agriculture, manages senior personnel, and requires extensive collaboration with governments, researchers, delivery partners, and private-sector organizations across low- and middle-income countries.

Leads technology risk and enterprise risk engagements, including IT audits, risk assessments, internal control testing, compliance programs, and advisory for technology transformations. Evaluates business processes and IT controls, documents findings, recommends improvements, manages engagement budgets and timelines, supervises junior staff, supports proposals and sales, and delivers client presentations and thought leadership.

Leads advanced omnichannel analytics for pharmaceutical brand and pipeline strategies, including campaign measurement, ROI, tactic optimization, promotional mix, and next-best engagement. Provides stakeholder consulting, reporting governance, actionable insights, statistical modeling, and process automation. Manages analytical projects, vendors, resources, timelines, and deliverables while collaborating cross-functionally in an Agile environment. The role also develops analytical methodologies, mentors colleagues, and ensures data quality, privacy, and compliance.

Leads targeted, end-to-end recruitment for niche Data, AI, and Data Science roles. Develops sourcing strategies, conducts proactive headhunting, maps talent markets, engages passive candidates, and builds technical talent pipelines. Partners with executive and engineering stakeholders, manages candidate relationships and offer negotiations, and provides market intelligence on skills, compensation, competitors, and talent availability. Requires sufficient technical familiarity to distinguish specialized data and AI skill sets during screening.

The Senior Engineering Manager will lead engineering teams to build scalable infrastructure and products for the Databricks platform, ensuring high technical standards and supporting engineers in their development while collaborating with product leadership.

Leads a data science organization responsible for strategy, production ML systems, model deployment, monitoring, scalability, and reliability. Partners with product, engineering, and business leaders to prioritize initiatives, improve ML platforms, guide research adoption, and resolve critical production issues. Mentors data scientists, drives technical and people development, and ensures experimentation rigor, cost efficiency, observability, and measurable business impact.

Assist with deploying AI and LLM applications, implementing and fine-tuning machine learning solutions, validating AI-generated outputs, supporting AI research, troubleshooting models and scripts, and documenting code and workflows. The role involves collaboration with senior team members and cross-functional teams, with opportunities to work on prompt engineering, LLM APIs, data processing, and NLP.

Manages a finance team supporting prepaid reconciliations and FITR AMS. Leads complex financial analysis, reporting, forecasting, accounting controls, compliance, and process improvements for a global finance function. Partners with business leaders, ensures accurate and timely financial results, develops employees, and drives standardization, simplification, elimination, and automation through advanced analytics, machine learning, and AI.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Lead design and implementation of scalable knowledge graph solutions for pharmaceutical R&D. Build graph data models, integrate heterogeneous scientific datasets, drive adoption of RDF/property-graph technologies, partner with platform and AI/ML teams, ensure performance and governance, define roadmap, and mentor engineers in ontology and graph engineering best practices.

Designs, develops, and validates control algorithms for temperature, pressure, flow, and vacuum systems used in semiconductor equipment. Responsibilities include process modeling, system identification, PID and advanced controller tuning, instrumentation integration, performance analysis, root-cause investigations, laboratory validation, diagnostics, predictive maintenance, and technical documentation. Collaborates with multidisciplinary engineering teams to deliver reliable, optimized automation and process-control solutions.

Lead and grow a team of applied researchers and engineers to build and operate production AI/ML systems (NLP, LLMs, recommender systems) at scale, set technical direction, partner cross-functionally, and drive measurable business and customer impact.

Design and implement scalable backend services, lead full software lifecycle, write design docs, mentor engineers, collaborate cross-functionally, improve performance and quality, and integrate AI tools to boost development productivity.

Designs, experiments with, deploys, monitors, and evaluates machine learning models. Requires full-stack development involving Python, React, AWS, APIs, and SQL, along with statistical and probabilistic foundations. The role supports AI/ML initiatives in a consulting environment serving financial services and energy clients.

Lead engineering strategy, teams, architecture, delivery, and platform outcomes for the Unify Energy SaaS platform. Oversee software, data, integrations, automation, security, reliability, and applied AI capabilities supporting oil and gas information workflows. Partner with product, customers, implementation, and business leaders to prioritize investments, improve delivery predictability, strengthen platform quality, and achieve measurable customer, operational, and financial results.

Lead enterprise-scale data engineering initiatives, building and optimizing batch, real-time, BI, and GenAI-ready pipelines on AWS. Develop data models, feature stores, and distributed processing solutions; support RAG, ML, and LLM workloads. Guide platform architecture, data governance, quality, risk mitigation, and roadmap planning while providing technical oversight across projects. Collaborate with analysts, leadership, Scrum Masters, and globally distributed teams using Agile, DevOps, DataOps, and MLOps practices.

Designs and operates scalable data-management services, APIs, connectors, event-driven integrations, metadata-as-code, data contracts, validation workflows, and CI/CD deployment patterns. The role automates governance, lineage, data quality, certification, schema-drift detection, and enterprise identifier mapping. It also applies AI and LLMs to metadata and data-quality workflows with human oversight. The position partners across engineering, product, architecture, governance, risk, and operations, supports production incidents, and leads teams delivering reusable capabilities across payments and asset-servicing domains.

Analyzes large datasets to solve business problems, build data science and machine learning models, automate dashboards, and generate actionable insights. The role involves SQL and NoSQL data work, experimentation, statistical analysis, visualization, root-cause analysis, and resolving data issues with engineering teams. The analyst collaborates with Product, Marketing, Operations, and Customer Experience stakeholders and presents findings to support executive strategy.

Develop NLP, computer vision, speech, deep learning, and machine learning solutions. Collaborate with AI researchers, lead experiments, mentor team members, conduct applied research, and deploy machine learning models. The role also involves developing and fine-tuning models, potentially including LLMs, and building AI capabilities for education platforms serving children across India.

Lead AI/ML research initiatives focused on developing advanced algorithms, diagnostics, prognostics, and modeling solutions for large-scale energy equipment. Responsibilities include architecting scalable models, analyzing complex datasets, designing experiments, building prototypes, publishing research, developing intellectual property, collaborating with cross-functional teams, and mentoring junior researchers. The role requires applying machine learning to industrial challenges while advancing research in control systems and signal processing.

Leads complex life sciences analytics projects from problem framing through recommendations, ensuring analytical rigor, quality, timeliness, and business impact. Works with real-world and commercial datasets, using Excel, SQL, R, Python, statistical modeling, AI, and machine learning. Responsibilities include client engagement, team leadership, mentoring, SOP development, capability building, proposal support, and deployment of AI-enabled analytics solutions.