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: ₹100,000.00 - ₹150,000.00 per month
Shift availability:
- Night Shift (Preferred)
Work Location: Remote