Top Machine Learning Jobs in India

Support accounting and external reporting by building and maintaining automation for data extraction, transformation, and validation; analyze large datasets for reporting anomalies; document automated processes; explore GenAI tools; collaborate with finance teams; assist projects and SOP preparation while ensuring adherence to financial policies.

Build and maintain AWS-based pipelines that transform raw production traces into high-quality LLM training datasets. Responsibilities include ingestion, deduplication, format conversion, quality filtering, identity-aware train/test splitting, schema validation, sampling, and continuous retraining-data processing. The role requires strong Python data engineering skills, large-scale JSONL processing, Hugging Face Datasets, Arrow-based storage, AWS services, and familiarity with conversational and tokenizer-specific data formats.

Fine-tune open-source LLMs from experimentation through production on AWS. Run SFT and alignment methods including GRPO and DPO, debug distributed multi-GPU training, resolve loss and memory issues, and collaborate with evaluation and data engineering teams to improve accuracy. Scale models from 8B to 70B parameters based on offline metrics.

Provides technical leadership for machine learning initiatives across eBay Ads and marketplace discovery. Designs, builds, and operates scalable production ML and backend systems for sponsored search, recommendations, ranking, retrieval, personalization, and GenAI. Partners with researchers to productionize algorithms, promotes engineering best practices, mentors engineers, and drives customer and business impact through experimentation, monitoring, alerting, and incident response.

Build and operate scalable machine learning platforms, data pipelines, APIs, and MLOps systems for eBay Ads. Develop and productionize traditional and generative AI models supporting search, recommendations, ranking, retrieval, and personalization. Own model training, deployment, monitoring, and reliability in 24/7 environments. Collaborate with researchers, product managers, and engineers to convert prototypes into low-latency production systems, while contributing to software engineering best practices and mentoring teammates.

Build and operate scalable machine learning platforms, data pipelines, MLOps workflows, APIs, and production services for eBay Ads. Develop and serve traditional and generative AI models supporting search, recommendations, ranking, retrieval, and personalization. Collaborate with researchers and engineers to productionize algorithms, improve reliability and performance, resolve incidents, and mentor team members.

Build and operate scalable backend and machine learning systems for sponsored search, recommendations, ranking, retrieval, personalization, and generative AI. Develop MLOps, data, training, validation, serving, monitoring, and CI/CD pipelines; optimize low-latency model services and APIs; productionize research prototypes; resolve reliability issues; and mentor engineers. Collaborate with applied researchers, product managers, and engineering teams to deliver production-ready ML capabilities at global ecommerce scale.

Design, deliver, and operationalize LLM- and generative AI-driven platform services. Build scalable model-serving, orchestration, MLOps, observability, data lineage, and evaluation frameworks to productionize multi-step automated workflows.

Per-diem breast pathologist responsible for diagnostic breast oncologic surgical pathology, collaborating with molecular pathologists and oncologists for precision oncology, supporting clinical trials, and participating in teaching and academic work including translational research or quality improvement.

Academic breast pathologist responsible for diagnostic breast oncologic surgical pathology, collaborating with molecular pathologists and oncologists for precision oncology, participating in teaching and mentoring, and engaging in clinical/translational research, digital pathology, and clinical trials support.

Lead industrialization and site construction for a new turbine and generator product line: manage teams, ensure EHS and ISO compliance, drive lean and smart-manufacturing integration, oversee quality, commissioning, scheduling, cost control, and new-product qualification to meet production targets.

Implement, validate, and deploy ML and rule-based models for industrial clients. Perform tag mapping from P&IDs/PFDs and control systems, ensure model quality through validation and QA, collaborate with Project Managers and Technical Consultants, automate model-building tasks, and provide feedback to improve product and deployment processes.

As a Senior Backend Engineer, you will design and maintain scalable software solutions, embrace innovation with AI tools, and ensure high-quality engineering practices.

Design, build, and automate secure AWS infrastructure and CI/CD pipelines. Ensure availability, performance, scalability, and compliance (PCI‑DSS, HIPAA, SOX, GDPR, CCPA). Manage releases/configuration, container and API security, serverless solutions, security automation/ML, monitor systems, and publish CICD best practices.

Design, build, and maintain backend APIs and services for a real-time supply chain visibility platform. Own system architecture, real-time event ingestion, data normalization, ML integration, monitoring, and production debugging while collaborating with Product and Data Science to deliver scalable, high-availability features.

Lead and oversee GAN solutions across the organization, drive high-impact GAN initiatives, ensure alignment with organizational goals, and provide executive-level strategic guidance.

Design, build and validate credit risk scorecards (application, behavioral, collections). Extract, clean, and preprocess data using SQL; develop ML models in Python. Monitor model performance, backtest and recalibrate to meet regulatory standards. Collaborate with business teams and document modeling processes for governance and audit.

Work on AI/ML/Deep Learning models for new use cases, gain exposure to real-world industry data, produce written artifacts (articles), participate in guided knowledge-transfer sessions, and potentially earn recommendation or a pre-placement offer based on performance.

Design, build, and deploy end-to-end ML/DL and statistical models to solve business problems (claims, underwriting, risk, servicing). Engage clients, lead stakeholder sessions, diagnose process inefficiencies, implement scalable pipelines, and communicate technical insights to deliver client-ready analytics solutions.

Lead development, validation, and monitoring of real-time fraud detection models using ML and deep learning. Perform EDA, root-cause analysis, optimize model performance and explainability, collaborate with fraud strategy and operations, and produce technical documentation and actionable insights for leadership.

Design and build AI-powered applications integrating OpenAI models (GPT, Codex, DALL·E). Define use cases with cross-functional teams, implement prompt engineering and fine-tuning, ensure responsible AI practices, monitor and optimize model performance, write maintainable code, participate in reviews, troubleshoot integrations, and stay current with AI research.

Lead analytics projects from planning through delivery: manage teams, gather stakeholder requirements, build SQL/Python solutions, apply ML methods, create dashboards and presentations, and mentor staff to drive data-driven decisions.

Lead large-scale data management, exploratory data analysis, and feature engineering using bureau data for credit risk modeling. Build, optimize, and document machine learning models, automate EDA/feature transformation in Python, and present data-driven insights to technical and non-technical stakeholders.

Perform marketing analytics for P&C insurance campaigns: translate business questions into analytical solutions, program end-to-end in Python/SAS, run EDA, build and QC statistical and machine learning models, and generate campaign performance reports to inform decision strategy.

Lead operational assessments and transformation engagements: redesign processes, drive continuous improvement (Lean/Six Sigma/Kaizen), implement digital automation (RPA, GenAI, ML), leverage advanced analytics and data visualization, manage stakeholders and change, deliver project governance, and measure impact via KPIs.