Hands-on AI & ML Development:
- Build, train, and deploy ML modelsfrom classical techniques (Classification, Regression, Forecasting) to advanced deep learning, LLMs, and Generative AI.
- Performfeature engineering, statistical analysis, and model tuningto enhance predictive capabilities.
- Work withbig data tools(Hadoop, PySpark, Hive) to process large datasets efficiently.
- DevelopAI-driven solutionstailored for industries like Retail, CPG, BFSI, Healthcare, and eCommerce.
- Optimize and automate machine learning pipelines for scalable deployment.
End-to-End AI/ML Deployment:
- Own thefull ML lifecycle, from development to deployment and monitoring.
- Collaborate withData Engineering teamsto build production-ready ML pipelines.
- Work with cloud platforms such asAzure, AWS, GCP, or Databricksfor model deployment and monitoring.
- Design and implementA/B testing strategiesto measure model impact.
Project Execution & Delivery:
- Drive multiple AI/ML initiativesfrom concept to execution, ensuring quality and timely delivery.
- Partner withcross-functional teams(Product, Data Engineering, Business) to translate business problems intoscalable AI solutions.
- Own project roadmaps, track milestones, and ensure stakeholder alignment.
- Communicate AI/ML concepts effectively toboth technical and non-technical stakeholders.
Leadership & Team Growth:
- Lead by doingmentor and guide a high-performance teamof data scientists.
- Set technical direction, review code, and establish best practices in AI/ML development.
- Foster a culture ofcontinuous learning, innovation, and hands-on problem-solving.
- Providecareer development guidanceand help team members achieve technical excellence.
What You Bring to the Table:
- 8+ yearsof hands-on experience inData Science & AI, with strong expertise in ML,Python, and SQL.
- Exposure to Generative AI Should have worked on at least a few POCs or pilot projects leveraging Gen AI capabilities
- Deep understanding ofML algorithms, including NLP, LLMs, forecasting, and optimization techniques.
- Proven track record ofbuilding and deploying machine learning modelsin production environments.
- Expertise in at least one cloud platform:AWS, Azure, GCP, or Databricks.
- Strong grasp ofstatistics, probability, and causal inference.
- Ability to break down complex AI concepts for business stakeholders.
- Experience withbig data tools(Hadoop, Hive, PySpark) andML workflow automation.
- Bonus: Experience withGoogle Analytics, Adobe Analytics, or digital marketing analytics.
