| Description | As a Data Scientist / ML Engineer you will own end-to-end data science machine-learning ETL dashboarding and conversational AI solutions on AWS. You’ll work on diverse consulting projects—helping clients explore data build predictive models operationalize workflows visualize insights and develop chatbots powered by large language models.Key Responsibilities Exploratory Data Science Conduct exploratory data analysis (EDA) statistical testing and feature engineering Prototype predictive models (classification regression clustering) using Python libraries (pandas scikit-learn statsmodels) Machine-Learning Engineering Develop train tune and evaluate models iterate on algorithm selection and hyperparameters Containerize model code (Docker) and package for deployment Collaborate with DevOps to implement CI/CD pipelines for continuous training and deployment ETL & Data Engineering Design build and maintain scalable ETL workflows with AWS Glue Step Functions and Lambda Ingest transform and load data into S3 Redshift Athena and/or EMR Implement data quality frameworks (e.g. Great Expectations) and monitoring (CloudWatch custom alerts) Data Visualization & Dashboarding Design and develop interactive dashboards and visualizations using Amazon QuickSight Translate complex data insights into intuitive stakeholder-friendly reports and dashboards LLM & Chatbot Development Design and develop conversational AI solutions using large language models (e.g. OpenAI GPT Anthropic LLaMA) Implement prompt engineering fine-tuning and evaluation to ensure accurate context-aware responses Integrate LLM-based chatbots with applications via APIs and frameworks (e.g. LangChain OpenAI API) Build and maintain vector search and retrieval-augmented generation (RAG) pipelines for knowledge retrieval MLOps & Production Monitoring Define infrastructure-as-code (CloudFormation or Terraform) for data and ML services Set up model versioning performance tracking and automated retraining triggers Document runbooks SLA expectations and rollback procedures Client Engagement & Knowledge Transfer Present findings and architecture designs to stakeholders Mentor client teams on best practices for data science MLOps dashboarding and conversational AI Required Qualifications 4+ years’ experience in data science or ML engineering roles preferably in a consulting environment Expert-level Python skills (pandas NumPy scikit-learn TensorFlow or PyTorch a plus) Hands-on experience with AWS services: Glue Step Functions Lambda S3 Redshift Athena EMR Proficiency in designing and operating ETL pipelines and data warehouses Experience building dashboards with Amazon QuickSight Experience with large language models and conversational AI development (OpenAI Anthropic Hugging Face) Familiarity with prompt engineering RAG and vector database technologies (Pinecone Weaviate) Familiarity with containerization (Docker) and CI/CD tools (GitLab CI/CD Jenkins or AWS CodePipeline) Strong SQL skills and understanding of data modeling principles Excellent communication skills and experience presenting to both technical and non-technical audiences Job Type: Part-time Pay: ₹100000.00 - ₹150000.00 per month Shift availability: Night Shift (Preferred) Work Location: Remote |