- Model Development & Statistical Analysis - Design, develop, and optimize machine learning, deep learning, and statistical models to solve complex business problems.

- GenAI & LLM Integration - Implement and integrate AI/ML models into GenAI and Agentic AI applications using LangChain, LangGraph, OpenAI APIs, RAG pipelines, and vector databases.

- End-to-End ML Pipelines - Build, deploy, and maintain scalable ML pipelines, from data ingestion and preprocessing to model deployment, monitoring, and lifecycle management.

- Production Deployment & MLOps - Deploy models as APIs or services using Docker, Kubernetes, FastAPI, and cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML) while implementing CI/CD for automated testing and versioning.

- Performance Monitoring & Reliability Monitor model performance in production, detect drift or degradation, and establish alerting mechanisms to ensure reliability, compliance, and governance.

- Analytics Integration Embed models into real-time dashboards and analytics applications to deliver actionable insights for business and HR stakeholders.

- Governance & Compliance Ensure model explainability (XAI), security, audit logging, and adherence to ethical, regulatory, and enterprise standards.

- Knowledge Sharing - Conduct and participate in training sessions, workshops, and technical discussions to share insights, AI/ML techniques, and best practices with team members.

Minimum Qualifications / Skills :

- Bachelors or masters degree in data science, Computer Science, Statistics, Mathematics, or a related quantitative field.

- 5 to 8 years of hands-on experience in data analysis, machine learning, or AI projects, preferably in corporate or academic settings.

- Certification in areas like GenAI, Databricks etc.

Preferred Qualifications / Skills :

- Hands-on experience in data preprocessing, exploratory data analysis, and feature engineering to support ML and AI model development.

- Knowledge of machine learning, deep learning, and NLP techniques for classification, regression, clustering, and predictive modelling.

- Proficiency in Python, SQL, ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn), and familiarity with cloud platforms (AWS, Azure, GCP) and data visualization tools.

- Experience integrating models into dashboards, analytics tools, and prototype AI applications.

- Familiarity with MLOps practices, model monitoring, CI/CD workflows, and cloud platforms (AWS, Azure, GCP).

- Ability to translate business problems into data-driven solutions and communicate insights to stakeholders effectively.

Notice Period- max 15-20 days