2,521 Sentiment Analysis jobs in India

Job Description -

We are seeking a Machine Learning Engineer to assist in developing and implementing object detection and AI-based predictions. You will have the opportunity to work on real-world applications and contribute to the development of novel algorithms. As a ML engineer, you’ll collaborate with our team and works various applied AI/ML tasks.

Key Responsibilities -

• Assist in training and fine-tuning ML models for real-time AI-based tasks.
• Work with large datasets to prepare, annotate, preprocess, and augment data for training purposes.
• Implement and test model architectures to improve accuracy, speed, and performance.
• Help analyze and optimize model performance based on results and metrics.
• Document research and findings, contributing to team knowledge and project reports.
• Participate in code reviews and contribute to software development best practices.
• Stay updated with the latest trends and research in computer vision and applied ML.

Qualifications -

• Bachelor's or master's degree program in Computer Science, Electrical Engineering, or a related field.
• Solid understanding of AI/ML concepts, data cleaning, synthetic data generation and other relevant concepts.
• Hands-on experience with MLOps.
• Hands-on experience with YOLO or similar deep learning object detection frameworks (e.g., Faster R-CNN, SSD).
• Proficiency in programming languages such as Python
• Experience with OpenCV and Pytorch is a must.
• Experience with Statistical Analysis and modeling is a plus.
• Experience with CUDA and GPU acceleration is a plus.
• Experience with ROS and C++ is also a huge plus.
• Strong problem-solving skills, attention to detail, and a collaborative mindset.

Preferred Skills -

• Knowledge of data cleaning techniques and data preprocessing methods.
• Familiarity with version control systems like Git .
• Experience in deploying models into production environments (optional).
• Exposure to ROS and OpenCV in C++.

Designation: - ML / MLOPs Engineer

Location: - Noida (Sector- 132)

Key Responsibilities:

• Model Development & Algorithm Optimization : Design, implement, and optimize ML

models and algorithms using libraries and frameworks such as TensorFlow , PyTorch , and

scikit-learn to solve complex business problems.

• Training & Evaluation : Train and evaluate models using historical data, ensuring accuracy,

scalability, and efficiency while fine-tuning hyperparameters.

• Data Preprocessing & Cleaning : Clean, preprocess, and transform raw data into a suitable

format for model training and evaluation, applying industry best practices to ensure data

quality.

• Feature Engineering : Conduct feature engineering to extract meaningful features from data

that enhance model performance and improve predictive capabilities.

• Model Deployment & Pipelines : Build end-to-end pipelines and workflows for deploying

machine learning models into production environments, leveraging Azure Machine

Learning and containerization technologies like Docker and Kubernetes .

• Production Deployment : Develop and deploy machine learning models to production

environments, ensuring scalability and reliability using tools such as Azure Kubernetes

Service (AKS) .

• End-to-End ML Lifecycle Automation : Automate the end-to-end machine learning

lifecycle, including data ingestion, model training, deployment, and monitoring, ensuring

seamless operations and faster model iteration.

• Performance Optimization : Monitor and improve inference speed and latency to meet real-

time processing requirements, ensuring efficient and scalable solutions.

• NLP, CV, GenAI Programming : Work on machine learning projects involving Natural

Language Processing (NLP) , Computer Vision (CV) , and Generative AI (GenAI) ,

applying state-of-the-art techniques and frameworks to improve model performance.

• Collaboration & CI/CD Integration : Collaborate with data scientists and engineers to

integrate ML models into production workflows, building and maintaining continuous

integration/continuous deployment (CI/CD) pipelines using tools like Azure DevOps , Git ,

and Jenkins .

• Monitoring & Optimization : Continuously monitor the performance of deployed models,

adjusting parameters and optimizing algorithms to improve accuracy and efficiency.

• Security & Compliance : Ensure all machine learning models and processes adhere to

industry security standards and compliance protocols , such as GDPR and HIPAA .

• Documentation & Reporting : Document machine learning processes, models, and results to

ensure reproducibility and effective communication with stakeholders.Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related

field.

• 3+ years of experience in machine learning operations (MLOps), cloud engineering, or

similar roles.

• Proficiency in Python , with hands-on experience using libraries such as TensorFlow ,

PyTorch , scikit-learn , Pandas , and NumPy .

• Strong experience with Azure Machine Learning services, including Azure ML Studio ,

Azure Databricks , and Azure Kubernetes Service (AKS) .

• Knowledge and experience in building end-to-end ML pipelines, deploying models, and

automating the machine learning lifecycle.

• Expertise in Docker , Kubernetes , and container orchestration for deploying machine

learning models at scale.

• Experience in data engineering practices and familiarity with cloud storage solutions like

Azure Blob Storage and Azure Data Lake .

• Strong understanding of NLP , CV , or GenAI programming, along with the ability to apply

these techniques to real-world business problems.

• Experience with Git , Azure DevOps , or similar tools to manage version control and CI/CD

pipelines.

• Solid experience in machine learning algorithms , model training , evaluation , and

hyperparameter tuning

Founded in the year 2017, CoffeeBeans specializes in offering high end consulting services in technology, product, and processes. We help our clients attain significant improvement in quality of delivery through impactful product launches, process simplification, and help build competencies that drive business outcomes across industries. The company uses new-age technologies to help its clients build superior products and realize better customer value. We also offer data-driven solutions and AI-based products for businesses operating in a wide range of product categories and service domains.

As part of our continued platform evolution and expansion into generative AI, we are looking for a skilled Machine Learning Engineer to join our team. You will be responsible for designing, building, and deploying machine learning models, with a focus on GenAI, Retrieval-Augmented Generation (RAG), large language models (LLMs), and intelligent chatbot integration. Your work will enable rapid development of innovative use cases contextualized to our client's domain, significantly reducing time-to-market and enhancing end-user experience.

Key Responsibilities:

• Design and develop machine learning and generative AI models to enhance platform capabilities, enable intelligent interactions, and support diverse use cases.
• Implement Retrieval-Augmented Generation (RAG) pipelines for contextual responses based on enterprise data.
• Integrate LLM-based chatbots with client-specific domain knowledge and data systems to ensure relevance and accuracy.
• Collaborate with Data Scientists, ML Engineers, and MLOps teams to operationalize models using Azure ML and other cloud-native services.
• Build and maintain scalable machine learning and RAG pipelines using Azure technologies.
• Fine-tune pre-trained LLMs and optimize model performance based on business and technical feedback.
• Continuously evaluate emerging techniques in generative AI, ML, and data science to recommend improvements and innovations.
• Work in an Agile environment and participate in team ceremonies and stakeholder interactions to align model development with business needs.

Required Skills and Experience:

• 4–9 years of relevant experience in AI/Machine Learning, Data Science, or a related field.
• Strong proficiency in Python and experience with ML libraries such as PyTorch, TensorFlow, HuggingFace Transformers, LangChain, etc.
• Hands-on experience with Azure ML, Azure Cognitive Services, Azure OpenAI, and related Azure data services (e.g., Azure Data Lake, Azure Functions, Azure Search).
• Experience building and deploying RAG architectures and working with LLMs for enterprise use cases.
• Understanding of vector databases (e.g., Pinecone, Weaviate, Azure AI Search with Vector Capabilities).
• Familiarity with chatbot development frameworks and integrating with front-end interfaces.
• Solid grasp of ML model lifecycle including versioning, monitoring, CI/CD, and MLOps principles.
• Experience working in collaborative, agile, and fast-paced environments.
• Databricks

Desirable Skills:

• Familiarity with large-scale data processing using Spark, or similar.
• Knowledge of containerization and orchestration tools like Docker and Kubernetes.
• Exposure to domain-specific fine-tuning of LLMs and prompt engineering practices.
• Strong communication and stakeholder collaboration skills to understand client context and explain technical approaches effectively.

Must Have Requirements

• Strong proficiency in Python and experience with ML libraries such as PyTorch, TensorFlow, HuggingFace Transformers, LangChain, etc.
• BFSI Domain Experience

• Develop, train, test, and deploy Machine learning models across various fields including computer vision, LLMs, and with tabular and time series data.
• Strong experience in Python, FastAPI, Flask
• Strong experience in SQL
• Strong experience Design Pattern/algorithms and data structures
• Familiarity with OOPS, Design Pattern/algorithms and data structures
• Familiarity with continuous integration, deployment, and automated build processes for scalable application delivery using Docker/Kubernetes
• Practical knowledge of one or more major cloud platforms (e.g. Azure, AWS, or GCP ).
• Excellent written and verbal communication skills in English.
• Experiment with novel deep learning-based technologies such as self-supervised learning and generative AI.
• Work directly with customer data and set up data pipelines to collect, curate, transform, and version data.
• Participate in the collection, analysis, interpretation, and output of large amounts of data using advanced AI techniques like deep learning, NLP, and computer vision good foundational experience in PyTorch / Tensorflow .
• Work within the global corporate Artificial Intelligence division, which addresses real business challenges and opportunities across multiple countries.
• Collaborate across different business and corporate functions in an international team composed of Project Managers, Data Scientists, Data and Software Engineers within the Artificial Intelligence team and others in the Global AI team

We’re looking for a hands-on ML Lead to drive development of scalable, high-performance GenAI applications, particularly using LLMs , RAG architectures , and prompt engineering . If you thrive in an agile, ownership-driven environment and want to shape foundational systems, this is the role for you.

What You’ll Do

• Lead the design and development of LLM-based applications , with a strong focus on prompt optimization , fine-tuning, and real-world usage.
• Architect and implement RAG pipelines using vector databases (e.g., FAISS, Pinecone) and retrieval frameworks (e.g., LangChain, LlamaIndex).
• Develop robust evaluation frameworks for prompt and model performance, including hallucination detection, latency, and relevance metrics.
• Own the end-to-end ML lifecycle : data pipelines, model experimentation, deployment, and monitoring.
• Work closely with founders, product managers, and engineers to align AI capabilities with core product needs.
• Mentor junior ML and data engineers, helping to grow a high-performing AI team.
• Stay at the forefront of GenAI research and rapidly evaluate and integrate new models and techniques.

Key Skills & Qualifications

Must-Have:

• Strong expertise in LLMs (e.g., OpenAI, Claude, Mistral, Llama, Falcon, Cohere).
• Deep knowledge of Prompt Engineering : prompt tuning, prompt chaining, and instruction design.
• Hands-on experience with RAG pipelines and tools like LangChain , Haystack , LlamaIndex .
• Proficient with vector databases (e.g., FAISS, Pinecone, Weaviate, Chroma).
• Advanced Python skills with ML libraries such as PyTorch , Transformers (Hugging Face) , and OpenAI API .
• Familiarity with MLOps and scalable model deployment practices (e.g., Docker, FastAPI, Streamlit, or Ray Serve).
• Strong grasp of NLP concepts, embeddings, tokenization, and evaluation metrics.

Nice-to-Have:

• Experience in startups or building 0→1 AI products.
• Prior work with multimodal models (text + image/audio).
• Knowledge of data labeling, model safety, and fine-tuning.

Why Join Us?

• Work directly with the founding team on GenAI core technology.
• Build real-world products that impact users daily.

Role: Machine Learning

Experience: 6-8 Years

Location: Mumbai / Ahmedabad / Chennai

• 5+ years of experience with data warehouse technical architectures, ETL/ ELT, reporting/analytic tools, and scripting.
• Extensive knowledge and understanding of data modeling, schema design, and data lakes.
• 4+ years of data modeling experience and proficiency in writing advanced SQL and query performance tuning with on Snowflake in addition to Oracle, and Columnar Databases SQL optimization experience.
• Experience with AWS services including S3, Lambda, Data-pipeline, and other data technologies.
• Experience implementing Machine Learning algorithms for data quality, anomaly detection and continuous monitoring, etc