Top Machine Learning Engineer Jobs in Mumbai

Architect and develop Python-based ML applications, services, tools, and web UIs supporting global VFX and animation workflows. Integrate ML solutions, build MLOps pipelines, package complex Linux software, improve observability and reliability, and enforce testing, security, documentation, and coding standards. The role also includes troubleshooting escalated issues, collaborating with engineers and artists, mentoring developers, delegating work, and delivering pragmatic solutions under tight deadlines.

Leads the company’s machine learning research and development strategy, focusing on multimodal generative models for video, audio, and language. Designs architectures and large-scale training pipelines, builds and mentors a high-performing ML team, and drives deployment of research innovations into production. Collaborates with product, engineering, and creative teams, establishes ML standards, evaluates emerging technologies, and represents the company through research publications and conferences.

Leads the design and implementation of scalable MLOps pipelines on GCP using Vertex AI, Kubeflow, Airflow, and MLflow. Automates model deployment, monitoring, retraining, logging, and orchestration while establishing CI/CD, quality, and architecture standards. Provides production support, troubleshooting, optimization, and root cause analysis; partners with data science, engineering, and business teams; researches emerging technologies; and mentors engineers.

Designs, develops, and maintains end-to-end MLOps pipelines and production machine learning solutions using GCP, Vertex AI, Airflow, Kubeflow, MLflow, and related technologies. Responsibilities include data transformation, deployment, monitoring, retraining, CI/CD, architecture design, production support, troubleshooting, platform improvement, mentoring, and collaboration with data, platform, and business teams.

Build and support production MLOps pipelines on GCP using Vertex AI, Airflow, Kubeflow, and related tools. Responsibilities include feature engineering, model deployment, monitoring, retraining, lifecycle management, performance optimization, CI/CD, documentation, and operational support. The role collaborates with data science, data engineering, cloud platform, and MLOps teams to deliver scalable machine learning solutions and establish reusable MLOps standards and best practices.

Support product development for homepage, user center, AI-generated interfaces, market pages, onboarding, personalization, and recommendation experiences. Coordinate with engineering, design, data, and business teams; organize requirements; track metrics; support experiments; analyze feedback; and help resolve issues. The role is part of a 3–6 month early-career accelerator program focused on blockchain products and AI-driven user experiences.

Support KYC program operations by creating and validating KYC records, sourcing internal/external data, keeping workflow tools current, meeting SLA targets, escalating high-risk findings, and assisting compliance reporting and projects.

Entry-level KYC Operations Analyst assisting AML monitoring, KYC record creation and validation, interacting with relationship and compliance teams, maintaining workflow tools, meeting daily targets, escalating high-risk cases, and supporting reporting and departmental projects.

Develop production-ready AI and machine learning applications using cloud AI services and large language models. Responsibilities include designing scalable solutions involving generative AI, deep learning, neural networks, NLP, chatbots, and image processing. The role serves as a subject matter expert, guides team decisions, mentors colleagues, collaborates across teams, and improves AI processes and systems.

Design, build, and evolve secure, scalable full-stack sales applications incorporating generative AI, intelligent workflows, APIs, data services, and cloud infrastructure. Lead architecture and technical design decisions, integrate AI models and retrieval systems, improve testing, CI/CD, observability, and production reliability, and mentor engineers. Collaborate with Product, AI/ML, Data, Security, Solution Architecture, and Engineering teams to deliver enterprise applications globally.

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 design and implementation of ML tools for scenario classification, anomaly detection, sensor data quality, behavior analytics, and automated reporting. Build scalable pipelines and automated data curation for camera, radar, and LiDAR; create SIL/HIL replay interfaces, ground-truthing and annotation tools, and offline benchmarking. Integrate ML modules into simulation, HIL/SIL, and CI/CD ecosystems, implement reproducible workflows (Docker, CI/CD, artifact versioning, automated tests), monitor model/data quality and mentor junior engineers while collaborating with verification and perception teams.

Analyze large-scale transactional, operational, and customer datasets; build visualizations, machine learning models, and experiments; translate complex findings into clear recommendations for operational teams and executives; and apply analytical software, databases, visualization tools, and programming languages in a fast-paced blockchain environment.

Develop, train, evaluate, and deploy small task-specific ML models for real-time English conversational skills (ASR, pronunciation, grammar, recommendations). Create datasets and task-specific evaluations, improve internal SOTA, mentor engineers, and contribute to hiring.

Design, build and deliver enterprise BI data products: ingest/transform data, develop scalable data models and ETL/ELT pipelines, and create high-performance Tableau dashboards. Optimize performance, ensure data governance and quality, enable analytics and ML operationalization, automate reporting, and collaborate with cross-functional stakeholders to translate business requirements into production-ready analytical solutions.

Drive enterprise sales growth for Akamai Cloud Infrastructure Services by developing new accounts, building strategic account plans, advising executive stakeholders, and identifying cloud, compute, and AI workload opportunities. Lead consultative cross-selling and upselling, manage complex infrastructure deals, forecast pipelines, negotiate contracts, and coordinate enterprise sales teams and technical specialists through close and adoption.

Lead presales Solution Architect for UiPath Intelligent Automation. Run discovery workshops, design scalable enterprise automation, deliver demos/PoCs, produce technical proposals, define governance and integrations, and mentor teams while driving CoE and adoption.

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

Conduct assessments in sales, marketing, and supply chain; analyze data to identify trends and solutions; collaborate globally to drive revenue and innovate solutions.

Manage client relationships, develop technology solutions, oversee project delivery, and lead a cross-functional team. Focus on business development and talent retention.

Lead end-to-end AI solutions for clients, manage project delivery and people, partner with sales for business development, collaborate with engineering/MLOps/DevOps, ensure data quality, model training, tuning, deployment, monitoring, and provide executive updates on status and risks.

12-month apprenticeship supporting AI product development: gather requirements, conduct user and market research, document user stories, assist backlog prioritization, track adoption metrics, collect feedback, support demos/training, and coordinate stakeholder communications to drive AI product adoption.

Build data infrastructure and reliable pipelines, analyze product usage, train traditional and LLM-based models, define product metrics, run experiments, and translate findings into product and go-to-market decisions. The role combines data science, data engineering, product analytics, machine learning, and stakeholder communication, with ownership across data modeling, evaluation, experimentation, and product insight generation.

Design, prototype, and deliver agentic AI solutions using ML and generative models. Partner with product, engineering, and delivery teams to build, evaluate, and deploy LLM-based solutions, run experiments and POCs, and communicate technical recommendations to stakeholders.

Lead AI initiatives, craft product vision, manage lifecycle, utilize data for decisions, and foster collaboration across teams.