Top Machine Learning Jobs in India

Own data quality and root-cause analysis for prediction issues: detect anomalies, trace sources, fix processes, automate QA/reporting with Python and SQL, maintain dashboards, and partner with Data Science, Engineering, Product, and Commercial teams to improve prediction performance and customer-facing analytics.

Lead multi-stream data and AI projects, own project plans, budgets and risks, run governance meetings, coordinate Architects/Data Engineers/AI teams, apply Snap's delivery methodology, manage senior stakeholders, and oversee multiple concurrent client engagements to ensure timely delivery and client satisfaction.

Lead and manage a KYC operations team to deliver AML monitoring, onboarding, refresh processes, quality reviews, MIS reporting, and process improvements while liaising with Compliance and senior stakeholders to ensure regulatory adherence.

Develop, validate, optimize, and productionize machine learning models and predictive analytics solutions using Python, SQL, Databricks, and Microsoft Fabric. Responsibilities include data processing, feature engineering, statistical experimentation, model monitoring, workflow automation, agentic solution development, and technical documentation. The role partners with data scientists and regional stakeholders, communicates insights clearly, supports platform operations, and contributes to reliable end-to-end deployment across APAC.

Lead enterprise AI strategy, client executive engagement, and commercial growth; build and run a global AI engineering organization; drive Agentic AI, LLMs, cloud-native platform delivery, governance, and responsible deployment across healthcare and life sciences.

The role involves conducting AI research, teaching AI-related courses, collaborating on healthcare data modeling, and securing external funding, while mentoring students.

Lead and grow the India engineering organization while remaining hands-on: participate in architecture and code reviews, mentor engineers, hire senior talent, and drive technical excellence across AI, data, and platform systems to build scalable, production analytics products.

ValGenesis is hiring software engineers at all levels, focusing on collaboration and innovation. Roles require 2 to 12 years of experience across various engineering disciplines.

Sisu is looking for a CTO co-founder with deep technical expertise in software architecture, product development, or machine learning to help build a startup from the ground up.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

The Senior Business Analyst will analyze and solve complex problems in the Insurance industry, lead project teams, gather requirements, and develop innovative solutions using advanced analytics and machine learning.

The Director of Data Science will lead the development of AI systems, manage a team, and drive knowledge sharing while ensuring project alignment with business goals.

Sell and position the FICO Platform to enterprise clients in consumer lending and retail banking. Build senior stakeholder relationships, uncover decision-management opportunities, collaborate with pre-sales and internal teams to design solutions, and drive long-term, profitable customer engagements. Manage quarterly and annual sales targets and travel up to 50% to engage customers across Southeast Asia and adjacent industries (fintech, telecom, media, entertainment).

The Senior Data Analyst will leverage data to inform strategic decisions, analyze complex datasets, develop predictive models, and communicate insights to stakeholders.

Lead AI/ML initiatives as an individual contributor: design and implement ML solutions, develop and maintain code, define architecture, guide data infrastructure, and collaborate with cross-functional teams to solve business problems.

Analyze supply chain KPIs, troubleshoot complex planning issues, develop process and system improvements, automate analytics and data governance, support users with system questions, and drive cross-functional continuous improvement.

Owns the roadmap, delivery, and adoption of AI-enabled Integrated Business Planning solutions for global wind manufacturing. Partners with Supply Chain, Operations, Finance, and Digital teams to drive automation, predictive insights, demand and supply planning improvements, and governance. Leads design, integration, and Agile delivery of scalable data and AI products while driving user adoption and continuous improvement.

Architect and scale production ML and LLM systems for Cohesity’s Gaia Emblem engine, including RAG pipelines, model serving, GPU-enabled Kubernetes infrastructure, distributed microservices, APIs, and data pipelines. The role establishes technical direction, engineering standards, observability, CI/CD, and infrastructure-as-code practices while mentoring engineers and partnering with Product, Applied ML, Data Engineering, and SRE teams. It also involves hands-on coding, system debugging, performance optimization, and evaluating emerging ML infrastructure technologies.

The role involves analyzing biological mass spectrometry data, developing software tools, and scaling workflows for AI and human consumption in drug safety assessment.

Lead end-to-end analytics and AI for Growth & CLM: build dashboards, tracking, AI/agentic workflows, and automation using Python, SQL and LLMs; partner with Product, Marketing, Risk and Ops to identify growth opportunities, run experiments, and drive measurable business optimizations.

Work with cross-functional teams to define and deliver analytics-driven business strategies and product requirements. Lead projects that improve pricing, shipping, conversion, supplier intelligence, and automation. Build and evaluate statistical/ML models, translate analytics into product outcomes, and communicate findings to executives.

Lead design and delivery of scalable BI and AI-driven automation for KYC operations. Build advanced Tableau dashboards, integrate ML models, ensure output governance, collaborate with engineering and business stakeholders, and mentor technical teams.

Lead data science initiatives using advanced statistical techniques and machine learning to produce actionable insights. Partner with cross-functional teams to design data-driven solutions, manage and ensure integrity of large datasets, develop predictive models to forecast trends, and communicate results to stakeholders while staying current on emerging technologies.

Design and lead enterprise-scale AI, machine learning, and Generative AI solutions from architecture through deployment. Build data pipelines, feature engineering frameworks, RAG applications, AI agents, and LLM-powered systems. Establish MLOps practices for versioning, experimentation, monitoring, and CI/CD. Partner with stakeholders on AI initiatives, ensure security and governance compliance, and mentor ML engineers, data scientists, and AI teams.

Build backend services, APIs, databases, and data pipelines for large-scale satellite imagery and geospatial analytics. Deploy and monitor self-hosted ML inference services in offline, air-gapped defense environments. Optimize batching, latency, throughput, GPU usage, observability, evaluations, and analyst-facing tooling while maintaining reproducible builds and secure deployment processes.