1354 Machine Learning Engineer jobs in Mumbai

ROMSについて

ROMSにご関心いただきありがとうございます。

まずは弊社コーポレートサイトより、会社・事業概要をご確認ください。

弊社は2019年に創業し、EC/小売り/物流事業者/メーカー様向けに高性能小型自動倉庫とロボットピックを中心とした小型自動化ソリューションを企画・開発・販売するスタートアップ企業です。

30㎡クラスの超小型無人店舗を祖業とし、その要素技術を活用して直近では物流センターや工場向けに業界最高効率の小型自動倉庫システム(NFC:Nano-Fuifillment Center)をはじめ、立体高速ピース仕分機(Nano-Sorter)やロボットピッキングシステム等を全て自社で開発し展開しています。

2022年9月にKDDI様と渋谷にクイックコマース向けの無人店舗RCS(Robotics Convenience Store)を開店、総額12億円の資金調達を完了、23年2月には平和島にラボを開設し、本格的にEC/物流事業者/メーカー様向けの倉庫内物流の自動化領域へ展開を開始しました。以下、掲載記事ご参照ください。

• Logistics Today記事
• LNEWS記事
• MHジャーナル記事

特に物流向けのソリューションとして、以下動画のNano-SorterやNano-Streamを矢継ぎ早に開発、展開を開始しており、2024年に複数社への導入が決定、今後1-2年を目途に海外へも事業展開をしていく計画です。また、市場への浸透を加速すべく新たなプロダクト開発も常に行っており、展示会やセミナー等で積極的に打ち出していく予定です。

• Nano-Sorter動画
• Nano-Stream動画

About ROMS
Thank you for your interest in ROMS.

First, please check our company and business profile on our corporate website.

We are a start-up company established in 2019, planning, developing, and selling high-performance small automated warehouse solutions and robot picking systems for e-commerce/retail/logistics/manufacturers.

Although we started with the development of ultra-compact autonomous stores in the 30㎡ class and, utilizing its elemental technologies, we have recently developed the industry's most efficient compact automated warehouse system for distribution centers and factories, as well as 3D high-speed piece sorting machines and robot picking systems, all in-house.

In September 2022, we opened an unmanned store for quick commerce, RCS (Robotics Convenience Store), in Shibuya with KDDI and completed a total of 1.2 billion yen in funding. In February 2023, we opened a laboratory in Heiwajima and started expanding to the area of warehouse automation for logistics, e-commerce, and manufacturers.

We already plan to implement our solutions for several companies in Japan and expand our business overseas in the next 1-2 years. We are also developing new products to accelerate market penetration, which we plan to actively promote at exhibitions and seminars this year.

Why ROMS?

• As a Data Scientist, we can offer you the opportunity to be involved in the development of new automated systems, platforms, and UIUX that will be used across the entire retail industry.
• You can learn from members who have worked on large-scale system development for major Japanese manufacturers.
• All ROMS engineers are involved right from the planning stage, allowing you to experience all aspects of the development process, from upstream to downstream.

Job Description
The Data Scientist will develop analytics, proposals, and tools to maximize store sales and operational efficiency based on order and inventory data from retailers that have implemented our NFC/RCS.

Examples Of Data To Be Handled Are

• Real-time sales data from dozens/hundreds of stores with more than 100 orders per store every day
• Real-time inventory data at each store
• User customer information data

Responsibilities

• Collaborate closely with various teams such as business development, robotics engineers, automation engineers, etc.
• Develop analysis and proposal tools based on client data
• Design, develop and evaluate other related/derivative systems
• Team development and recruitment

Development environment

• Language : Python
• Machine Learning : Sklearn, PyTorch/Keras/TensorFlow
• DB :SQL (PostgreSQL, MySQL, MongoDB)
• Cloud :Amazon Web Services, Microsoft Azure, Google Cloud Platform
• Other :Docker, GitHub, Confluence

Skills
Required

• A willingness to create new things on your own.
• At least 3 years of experience in Python software development
• Interest in the retail industry

• English: Business level or above

• Japanese language skills are not required.

Welcomed

• Experience in data analysis and tool development in the retail industry
• Japanese: Business level or above

職種 / 募集ポジション Machine Learning Engineer (English) 雇用形態 正社員 給与 応相談 勤務地

会社情報 会社名 株式会社ROMS Corporate Site LinkedIn note X

• Apply Now

Profile: Machine Learning Engineer

Skills:Python, GenAI, Cloud services is AWS lamda, stepfunctions, LLm, CI/CD, Kibana, S3

Experience - 8+ Years

Time- 3 Hrs Per Day (Between 7 PM IST to 1 AM IST)

Location: Remote

About Neo Group:

Neo is a new-age, focused Wealth and Asset Management platform in India, catering to HNIs, UHNIs and multi-family offices. Neo stands on its three pillars of unbiased advisory, transparency and cost-efficiency, to offer comprehensive, trustworthy solutions. Founded by Nitin Jain (ex-CEO of Edelweiss Wealth), Neo has amassed over USD 3 Billion (₹25,000 Cr.) of Assets Under Advice within a short span of 2 years since inception, including USD 360 Million (₹3,000 Cr.) Assets Under Management. We have recently partnered with Peak XV Partners via a USD 35 Million growth round.

To know more, please visit:

Position:
Machine Learning Engineer

Location:
Mumbai

Experience:
1-6 years

About the Role

We are looking for an Machine Learning Engineer with a strong background in Machine Learning, NLP, and Generative AI to join our team. The ideal candidate will have hands-on expertise in building, deploying, and managing ML solutions, along with a deep understanding of modern AI frameworks and cloud-based MLOps practices.

Key Responsibilities:

• Design, develop, and deploy machine learning models and pipelines for production-scale applications.
• Implement NLP solutions using algorithms such as TF-IDF, Word2Vec, BERT, Zero-Shot and Few-Shot learning techniques.
• Apply Generative AI models (OpenAI, LLaMA, Gemini, Perplexity etc.) to solve real-world business problems.
• Collaborate with cross-functional teams to translate business requirements into technical solutions.
• Work with statistical and mathematical models to analyze complex datasets.
• Develop and maintain RESTful APIs and microservices using Flask/FastAPI.
• Manage ML lifecycle, including versioning, CI/CD, and deployment using AWS, Docker, Jenkins, and other MLOps tools.
• Maintain source code repositories (Git, Bitbucket) and ensure best practices for version control.
• Work with databases (MongoDB, MySQL) to handle structured and unstructured data.
• Participate in project management using tools like JIRA/Asana.
• Stay up-to-date with advancements in AI/ML and contribute to innovation within the team.

Required Skills & Experience

Education:

• Bachelor's/Master's in Computer Science, Engineering, Mathematics, or Statistics.
• ML/Deep Learning/NLP Certification preferred.

Technical Expertise (Must-Have):

• Programming: Python (DSA)
• Machine Learning & Statistics: Strong knowledge of algorithms (Linear Regression, Decision Trees, KNN, SVM, Random Forest, Logistic Regression, Bayesian methods, Mean-based approaches)
• NLP: Zero-shot, Few-shot classification, TF-IDF, Word2Vec, BERT
• Generative AI: OpenAI models, LLaMA, etc.
• Databases: MongoDB & MySQL
• Version Control: Git, Bitbucket
• Microservices: Flask, FastAPI
• MLOps: AWS, Jenkins, Docker/CircleCI
• Project Management Tools: JIRA, Asana
• REST APIs

Preferred Qualities

• Strong problem-solving and analytical skills.
• Ability to work independently as well as in a collaborative team environment.
• Excellent communication skills to convey complex technical concepts to non-technical stakeholders.
• Passion for staying ahead in the AI/ML space.

Why Join Us?

• Opportunity to work on cutting-edge AI and Generative AI projects.
• Exposure to end-to-end ML lifecycle and large-scale deployments.
• Collaborative and innovative work culture.
• Growth opportunities in leadership and advanced AI research.

Overview:

The Machine Learning Engineer will design, build, and productionize ML solutions that improve business outcomes. You'll contribute to end-to-end model development, collaborate with cross-functional teams, and ship reliable, well-tested models and services that make a measurable impact.

Prodege:

A cutting-edge marketing and consumer insights platform, Prodege has charted a course of innovation in the evolving technology landscape by helping leading brands, marketers, and agencies uncover the answers to their business questions, acquire new customers, increase revenue, and drive brand loyalty & product adoption. Bolstered by a major investment by Great Hill Partners in Q4 2021 and strategic acquisitions of Pollfish, BitBurst & AdGate Media in 2022, Prodege looks forward to more growth and innovation to empower our partners to gather meaningful, rich insights and better market to their target audiences.

As an organization, we go the extra mile to "Create Rewarding Moments" every day for our partners, consumers, and team. Come join us today

Primary Objectives:

• Applied ML Model Development & Evaluation
• Data Preparation, Feature Engineering & Experimentation
• Productionization of Models with Monitoring & Iteration
• Cross-Functional Collaboration with Data, Product & Engineering
• Code Quality, Testing, and Documentation
• Performance Tuning and Practical Problem Solving (80/20 focus)

Qualifications - To perform this job successfully, an individual must be able to perform each job duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Detailed Job Duties:

• Implement ML models (classification, regression, ranking, NLP, or recommendation) using Python and common ML libraries (e.g., scikit-learn, XGBoost; familiarity with PyTorch or TensorFlow).
• Prepare and analyze datasets; build features, conduct exploratory analysis, and design experiments with clear success metrics.
• Train, validate, and compare models; document methodology, trade-offs, and results; select approaches grounded in business goals.
• Package models for deployment (APIs, batch jobs, or streaming) in partnership with software/data engineering; add basic telemetry for performance and drift.
• Write clean, maintainable, and testable code; participate in code reviews and follow version control and branching standards.
• Monitor and iterate on model performance in production; troubleshoot issues and implement improvements.
• Collaborate with stakeholders to translate requirements into measurable ML deliverables; communicate progress, risks, and findings.
  Contribute to lightweight automation (notebooks scripts- jobs) and reusable utilities that improve team velocity.

What does SUCCESS look like?

Success means shipping reliable ML features to production that measurably improve KPIs (e.g., accuracy, latency, revenue lift, fraud reduction) while maintaining code quality and clear documentation. You're known for thoughtful experimentation, practical solutions, and effective collaboration that helps the team deliver value faster.

The MUST Haves:

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related field (or equivalent practical experience).
• Three or more (3+) years of hands-on experience applying machine learning in production settings.
• Strong proficiency in Python; solid software engineering fundamentals (testing, modular design, version control); familiarity with SQL.
• Practical experience with core ML techniques and algorithms (e.g., tree-based models, linear/logistic regression, clustering) and model evaluation.
• Working knowledge of one deep learning framework (PyTorch or TensorFlow) and when to use it vs. classical ML.
• Experience building data pipelines or jobs to train/score models; familiarity with Spark or similar is a plus.
• Exposure to deploying models (batch or real-time) and monitoring basic health/performance metrics.
• Clear written and verbal communication skills; ability to partner with product, engineering, and analytics.
• Strong analytical/problem-solving skills and a bias toward practical, timely solutions (80/20).

The Nice to Haves:

• Master's degree AI, Machine Learning, or related fields is a plus.
• Experience with cloud ML tooling (AWS, GCP, or Azure), MLflow/model registries, or basic MLOps practices.
• Working knowledge of APIs and microservices concepts; comfort containerizing workloads (Docker).
• Familiarity with NLP/LLM tooling (e.g., Hugging Face, embeddings, retrieval) and prompt or fine-tuning workflows.
• Advanced degree in a quantitative field, or relevant certifications (cloud/ML).

This opening is for Nova (not Fomogo).

While the role is posted via Fomogo's page, you'll be joining the
Nova team
, building AI teammates that help businesses protect revenue, prevent losses, and eliminate repetitive work.

About Nova:

Nova is building AI teammates that help businesses protect revenue, prevent invisible losses, and free teams from repetitive work. We focus on high-impact areas like reconciliations, contract reviews, and compliance tracking; turning manual tasks into ROI-driven automation that empowers people to do their best work.

About the Role

We're looking for an experienced Machine Learning Engineer (MLE)
to lead our ML efforts end-to-end
. This role involves architecting agentic AI workflows and optimizing model pipelines in production. You will own how our AI backbone scales, collaborate with backend and product teams, and ensure our agents are powerful, reliable, cost-efficient, and explainable. This is
a hands-on role
where you will build, guide, mentor, and influence the ML direction at Nova.

Role Responsibilities

• Architect and scale ML pipelines powering Nova's AI teammates.
• Implement multi-LLM, multi-agent orchestration
  (e.g.,
  LangGraph
  or equivalents) with routing, chaining, retries, tool-use, guardrails, and persisted state.
• Design and optimize
  RAG
  pipelines including hierarchical chunking, embeddings, hybrid search, and re-ranking.
• Build and maintain connectors so agents can reliably discover and integrate with external systems.
• Deploy and monitor models in production using APIs, tracing, metrics, and balancing cost with performance.

Required Qualifications

• 4–5 years as an ML Engineer / Applied Scientist working in production environments.
• Strong Python (FastAPI/Flask), PyTorch/TensorFlow, and data pipeline expertise.
• Deep knowledge of LLMs, RAG systems, embeddings, and vector DBs (PGVector/Postgres preferred).

Preferred Qualifications

• Experience working with multiple LLM providers (OpenAI, Anthropic, Google, open-source models) and balancing cost, latency, and quality.
• Comfortable with Docker, Kubernetes, CI/CD, and deploying to AWS/GCP.

Good to Have

• Experience with LangGraph/LangChain, vLLM/Ray.
• Familiarity with LLM evaluation frameworks or event-driven architectures.

What you will get:

A chance to build from 0 to 1 the AI backbone of a product targeting a $100M+ ARR opportunity. End-to-end ownership and visibility, your code will run in production, not in notebooks. A front-row seat to shape how agentic AI gets applied to real-world, high-stakes problems.