Top Machine Learning Jobs in India

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.

Lead the design, deployment, governance, and monitoring of AI and machine learning systems, including LLM agents, predictive models, computer vision, and document extraction. Establish engineering standards for prompts, evaluation, observability, cost management, model risk, and production metrics. Mentor engineers, set technical direction, assess emerging techniques, and communicate AI capabilities and trade-offs to business stakeholders.

Own the Jobs platform roadmap and coordinate architecture, engineering, data science, analytics, and product teams. Improve job ingestion, cleansing, indexing, search, matching, recommendations, data quality, vendor-feed coverage, and evaluation models at web scale. Define metrics, build dashboards, translate business requirements into end-to-end solutions, and ensure job seeker needs are met across millions of jobs and users.

Develops reactor, kinetic, process, and analytics models for refinery and petrochemical technologies. Analyzes plant data, forecasts yields, monitors catalyst performance, validates models, and supports optimization and commercialization. Collaborates with R&D, engineering, software, technical services, sales, and customers to define KPIs and deliver modeling solutions. Requires strong reaction engineering, process simulation, statistical analysis, programming, and refinery operations expertise.

Develops reactor, kinetic, process, and analytics models for refinery and petrochemical technologies. Responsibilities include yield estimation, catalyst-life prediction, process optimization, plant-data analysis, model validation, KPI development, and technical collaboration with R&D, engineering, software, technical services, and customers. The role requires strong reaction engineering, process simulation, statistics, programming, numerical methods, and refinery operations expertise, with opportunities to advance modeling techniques and digital solutions.

Conduct quantitative analyses, statistical modeling, machine learning, data engineering, and predictive modeling for pharmaceutical development, manufacturing, quality, and lifecycle decisions. Translate scientific questions into reproducible analyses, build data solutions and analytical tools, communicate findings, support regulatory documentation, and lead defined workstreams. Collaborate onsite with scientists, engineers, manufacturing, analytical, quality, regulatory, and digital teams while mentoring colleagues and improving CMC decision-making practices.

Lead a team of layout engineers developing SRAM, RF, ROM, and CAM memory compiler designs for advanced FinFET and GAA technologies. Oversee floorplanning, placement, routing, physical verification, yield, DFM, parasitic extraction, and reliability signoff. Drive layout methodology innovation and automation using SKILL, Python, Tcl, AI, and machine learning. Mentor senior engineers, manage resources, collaborate with circuit, tiling, and foundry teams, and optimize memory area, performance, power, and yield.

Build privacy and anonymization systems that detect and transform PII, credentials, secrets, and other sensitive information in data used for AI training. Develop detection methods combining rules, statistical models, classifiers, and LLMs; create production data-processing pipelines; evaluate privacy risk and retained utility; and ensure robustness against schema drift, unusual formats, leakage, and adversarial re-identification.

Research Engineer applying frontier machine learning and reinforcement learning research to production AI capabilities. Responsibilities include model design, training, evaluation, RLHF, multi-objective optimization, LLM post-training, reward modeling, auditing, and translating prototypes into reliable systems. The role also involves customer engagement, technical communication, and shaping research and product strategy.

Develop and deploy generative AI and machine learning applications using Python, LLMs, RAG pipelines, prompt engineering, embeddings, vector databases, and AI frameworks. Build REST APIs, process datasets, evaluate and optimize models, and assist with cloud and Docker deployments. The role requires foundational machine learning knowledge, practical project or internship experience, and familiarity with modern AI development tools.

Build and deploy Generative AI solutions, including LLM applications, autonomous agents, multimodal systems, RAG pipelines, and machine learning models. Lead projects from ideation through deployment, fine-tune models, develop multi-agent architectures, optimize NLP applications, use vector databases and LLM APIs, and research emerging AI methodologies. Collaborate with cross-functional teams to deliver chatbots, copilots, automation tools, and supply-chain solutions.

Support enterprise SaaS and AI/ML platforms by triaging incidents, analyzing LLM outputs and system traces, performing root-cause analysis, monitoring cloud services, validating data with SQL, inspecting APIs, and communicating resolutions to customers. Maintain documentation, improve runbooks, collaborate with engineering and data science teams, and participate in weekend and holiday on-call rotations.

Develop statistical and machine learning models, perform hypothesis testing, exploratory data analysis, root cause and factor analysis, and generate business insights. Extract and prepare data from enterprise data warehouses and big data platforms, train and improve models, monitor performance, and collaborate with clients across industries to solve complex business problems.

Develop and deploy end-to-end data science and machine learning solutions for banking use cases, including cross-sell, up-sell, attrition, and hyper-personalization. Consult with stakeholders, identify opportunities in structured and unstructured data, improve ML delivery processes, and communicate technical findings clearly. The role requires production ML experience, generative AI application, predictive modeling, and strong Python, PySpark, and SQL skills.

Develop and validate quantitative models for market analytics and transaction cost analysis using tick-by-tick market data and execution logs. Conduct statistical analysis, define model performance metrics, monitor deployed models, and partner with engineers to productionize research prototypes. The role also involves documenting methodologies, peer-reviewing analyses, following academic and industry developments, and communicating model assumptions and limitations to stakeholders.

Lead technical support for B2B customer deployments, diagnosing issues across ML models, applications, APIs, infrastructure, networking, and deployment environments. Investigate incidents, review logs and configurations, coordinate escalations with FDE, SRE, and Engineering teams, monitor deployment health, document root causes, communicate with customer stakeholders, and develop tools and dashboards to meet support SLOs.

Build scalable, secure backend infrastructure and large-scale data pipelines for latency-critical analytical workloads. Design cost-efficient cloud systems, collaborate with customers and founders on product and engineering priorities, and help shape the company’s engineering culture and technology stack.

Serve as a trusted technical advisor to strategic enterprise accounts, designing and implementing Databricks-based data, analytics, AI, and machine learning architectures. Lead proofs of concept, guide build-versus-buy decisions, integrate cloud and third-party services, support sales strategies, and collaborate with product and engineering teams. Build customer relationships, promote open-source projects, and provide architectural guidance across global accounts.

Designs and documents scalable enterprise solutions, leads project architecture, guides engineering teams, and ensures integration, performance, quality, and compliance with enterprise standards. Collaborates across product, strategy, engineering, and architecture teams; evaluates SaaS vendors; contributes to technology roadmaps and reusable architecture patterns; and drives responsible AI and machine learning adoption within the domain.

Develop and deploy AI, generative AI, and LLM solutions that support credit risk modelers. Build and validate credit risk models using GCP, SAS, R, and Python; apply statistical and machine learning techniques; analyze results; document modeling processes; support model development; conduct ad hoc analytics; and implement secure, governed AI tools for analytical workflows.

Develop and execute system test plans and cases, automate testing with frameworks and scripting, use AI-based testing and basic ML insights, analyze results and logs, document defects, troubleshoot root causes with developers, and maintain test scripts and procedures.

Provides hands-on technical leadership for a reconciliation service platform. Designs scalable, secure, cloud-native architectures; develops full-stack software using Java and Spring Boot; builds RESTful and streaming APIs; applies distributed systems, data, testing, observability, and reliability practices; mentors engineers; creates reusable tooling and guardrails; and integrates AI-enabled capabilities responsibly.

Leads engineering operational excellence and transformation across global NPI and engineering value streams. Builds dashboards, KPI trackers, predictive models, and statistical analyses; promotes AI-driven automation and digitalization; supports Lean, Six Sigma, Kaizen, and value stream mapping initiatives; maintains process governance; and communicates productivity, cycle-time, quality, and risk insights to global stakeholders.

Own end-to-end delivery and expansion of agentic AI deployments for strategic enterprise accounts. Serve as a trusted advisor to executive stakeholders, coordinate Engineering, Product, Customer Success, and Sales, manage risks and milestones, quantify business value, identify account growth opportunities, and provide structured customer feedback to product leadership. Initially operate as a player-coach while building delivery processes and hiring a team.

Build foundational AI and machine learning capabilities for Twilio’s Conversation Memory team. Responsibilities include researching LLM orchestration, retrieval, personalization, and autonomous agents; designing rapid experiments; and developing scalable, production-grade ML systems using cloud services, high-volume data, streaming, and real-time inference. The role also requires technical leadership through mentorship, design reviews, and engineering standards without formal management authority.