Senior Data Scientist

RapidBrains; Pune District; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

RapidBrains seeks a Senior Data Scientist with deep expertise in Machine Learning, Recommender Systems and Generative AI. You will develop, deploy, and scale enterprise AI/ML solutions in collaboration with Data Engineering, ML Engineering and business stakeholders.

You will build end-to-end ML solutions, recommendation engines, and GenAI applications that are scalable, reliable, and governable, including model lifecycle, monitoring, and API exposure for production use.

Qualifications

• 5–8 years of experience in Data Science / ML or AI.
• 1
• Strong hands-on Python and ML frameworks.
• Experience with at least one cloud ML platform (AWS SageMaker / Azure ML / Vertex AI).
• Hands-on experience with RAG, prompt engineering, and LLM frameworks.
• Experience collaborating with Data Eng, ML Eng, and business teams.

Responsibilities

• Develop and deploy end-to-end ML models from ideation through production.
• Perform EDA, feature engineering, modeling, and evaluation.
• Build predictive and prescriptive models for business impact.
• Design scalable ML pipelines for training, testing, deployment.
• Develop production-ready ML services and APIs.
• Design and develop recommendation systems and personalization strategies.
• Implement model monitoring, governance, and retraining.

Skills

ML model development

Python

Recommender systems

GenAI / LLM

Prompt engineering

RAG architectures

Cloud ML platforms

Model monitoring

Explainability

Education

Bachelors or higher in Data Science / AI

Tools

AWS SageMaker

Azure ML

Vertex AI

Spark

Databricks

Docker

Kubernetes

LangChain

LlamaIndex

Job description

Notice Period: Immediate joiners preferred ( 0 to 30 days)

We are seeking a highly skilled Senior Data Scientist with strong expertise in Machine Learning, Recommender Systems, and Generative AI (LLM/LVM). The ideal candidate will have hands‑on experience developing, deploying, and scaling enterprise‑grade AI/ML solutions and working closely with Data Engineering, ML Engineering, and business stakeholders.

The role involves building end‑to‑end machine learning solutions, recommendation engines, and GenAI applications while ensuring scalability, reliability, monitoring, and responsible AI practices.

Key Responsibilities

Data Science & Advanced Analytics

• Develop and deploy end‑to‑end ML models from ideation through production.
• Perform EDA, feature engineering, predictive modeling, and model evaluation.
• Build predictive and prescriptive models using statistical and machine learning techniques.
• Optimize models for accuracy, performance, scalability, and business impact.
• Design and implement scalable ML pipelines for training, testing, validation, and deployment.
• Work with cloud ML platforms such as Azure ML, AWS SageMaker, and Google Vertex AI.
• Implement model lifecycle management, including versioning, monitoring, retraining, and governance.
• Develop production‑ready ML services and APIs.
• Design and develop collaborative, content‑based, and hybrid recommendation systems.
• Build ranking and personalization solutions using large‑scale datasets.
• Implement user segmentation and recommendation strategies.
• Evaluate recommendation models using metrics such as Precision@K, Recall@K, and NDCG.

Generative AI – LLM & LVM

• Build and deploy LLM‑powered applications, including chatbots, copilots, document intelligence, and intelligent assistants.
• Design and implement RAG (Retrieval-Augmented Generation) architectures.
• Work with models and platforms such as OpenAI, Azure OpenAI, Llama, and Hugging Face.
• Develop solutions for text generation, summarization, classification, and multimodal/image/video understanding.
• Implement prompt engineering and optimise prompts for production use cases.
• Work with frameworks such as LangChain and LlamaIndex.

Data Engineering Collaboration

• Define data requirements and collaborate with Data Engineering teams on data pipeline design.
• Ensure data quality, availability, governance, and reliability.
• Work with Apache Spark, Databricks, Hadoop, ETL pipelines, and data warehousing technologies.
• Deploy ML and AI models through APIs and microservices.
• Containerize applications using Docker and Kubernetes.
• Integrate ML solutions into enterprise CI/CD pipelines.
• Collaborate with ML Engineering and DevOps teams to support production deployments.
• Monitor model performance, drift, degradation, and reliability.
• Implement logging, alerting, and model monitoring mechanisms.
• Apply explainability and interpretability techniques.
• Ensure responsible AI practices covering fairness, transparency, governance, and bias mitigation.

Required Qualifications & Experience

• 5–8 years of experience in Data Science, Machine Learning, Artificial Intelligence, or a related field.
• Proven experience in end‑to‑end ML model development, deployment, and production support.
• Strong hands‑on experience with Python and modern ML frameworks.
• Experience with at least one cloud ML platform:
• AWS SageMaker
• Hands‑on experience with RAG, prompt engineering, and LLM frameworks.
• Experience collaborating with Data Engineering, ML Engineering, and business teams.