Role Summary
We are looking for a highly analytical and technically strong Data Science, Machine Learning & AI Engineer who can transform business and operational data into actionable insights, predictive models, and AI-powered solutions.
The role will involve the complete AI/ML lifecycle — from data collection, preprocessing and exploratory analysis to model development, evaluation, deployment, monitoring and optimization.
The candidate should be comfortable working with Python, machine learning frameworks, SQL, APIs, cloud environments and modern Generative AI technologies.
Key Responsibilities1. Data Science & Analytics
- Collect, clean, transform and analyze structured and unstructured data.
- Perform exploratory data analysis and identify meaningful patterns and trends.
- Develop statistical models and data-driven solutions for business problems.
- Create dashboards, reports and analytical models where required.
- Translate business requirements into measurable data science problems.
- Identify opportunities where AI/ML can improve business performance.
2. Machine Learning
- Develop supervised and unsupervised machine learning models.
- Work on classification, regression, clustering, recommendation and forecasting problems.
- Perform feature engineering and feature selection.
- Train, validate and optimize ML models.
- Evaluate models using appropriate performance metrics.
- Perform hyperparameter tuning and model optimization.
- Address issues such as overfitting, underfitting, data imbalance and model drift.
3. AI & Generative AI
- Develop and integrate AI-powered applications.
- Work with LLMs, NLP, embeddings, vector databases and Retrieval-Augmented Generation (RAG).
- Build AI chatbots, intelligent assistants, document-processing systems and automation solutions.
- Work with prompt engineering and LLM evaluation.
- Integrate commercial and/or open-source AI models through APIs.
- Explore and implement emerging AI technologies relevant to business requirements.
4. Model Deployment & MLOps
- Deploy machine learning and AI models into production.
- Build APIs and services for model inference.
- Work with Docker, CI/CD and cloud platforms.
- Implement model monitoring and performance tracking.
- Maintain model versioning and reproducibility.
- Troubleshoot production issues and improve model reliability.
5. Programming & Engineering
- Develop production-quality Python code.
- Write efficient SQL queries and work with databases.
- Develop reusable ML/AI pipelines and components.
- Integrate AI/ML solutions with existing applications and systems.
- Follow software development best practices, coding standards and documentation practices.
6. Research & Innovation
- Stay updated with developments in AI, ML, GenAI and data science.
- Evaluate new frameworks, models and tools.
- Build POCs to validate new AI/ML use cases.
- Recommend technologies based on business value, scalability and feasibility.
7. Collaboration
- Work closely with product, engineering, business and operations teams.
- Understand business problems and convert them into technical solutions.
- Present analytical findings and model outcomes to technical and non-technical stakeholders.
- Mentor junior team members where applicable.
Technical Skills RequiredMust Have
- Python
- SQL
- Pandas / NumPy
- Scikit-learn
- Machine Learning fundamentals
- Statistics and probability
- Data preprocessing and feature engineering
- Model evaluation and optimization
- Git/GitHub
- REST APIs
Good to Have
- TensorFlow / PyTorch
- NLP
- Computer Vision
- Time-series forecasting
- XGBoost / LightGBM
- Docker
- AWS / Azure / GCP
- MLflow
- Airflow
- Kubernetes
Generative AI Skills
- LLMs
- Prompt Engineering
- RAG
- Embeddings
- Vector databases
- LangChain / LlamaIndex
- OpenAI or other LLM APIs
- AI Agents
- LLM evaluation
- Fine-tuning / LoRA, where applicable
Pay: ₹15,000.00 - ₹20,000.00 per month
Work Location: In person
