• Architected and deployed end-to-end AI systems for enterprise workflow automation, transforming unstructured service tickets and logs into intent classification, entity extraction, semantic retrieval, and next-best-action recommendations used by downstream automation services.

• Built scalable data ingestion and feature engineering pipelines using Python and SQL, applying NLP preprocessing and metadata enrichment while enforcing strict training–serving feature parity, reducing model skew and improving production reliability.

• Fine-tuned transformer-based NLP models using PyTorch and Hugging Face for intent classification and NER, improving model accuracy while optimizing inference latency through batching, model selection, and runtime tuning.

• Implemented semantic search and context enrichment by generating dense embeddings and indexing them with FAISS, enabling similaritybased retrieval over historical tickets and knowledge articles and outperforming keyword search for recommendation relevance.

• Developed applied ML models beyond NLP, including time-series forecasting and anomaly detection pipelines (classical ML and deep learning), and integrated LLM-based document intelligence to automate analysis of operational and contractual data.

• Productionized ML inference at scale using FastAPI, Docker, and Kubernetes, integrating CI/CD, MLflow model versioning, monitoring, and rollback strategies to support low-latency, enterprise-grade deployments.

• Developed and deployed predictive models on large scale HR data to forecast attrition, retention, optimize recruitment and improve workforce planning through scalable ML solutions and interactive dashboards.

• Built predictive models (logistic regression, random forests, XGBoost) to forecast attrition, identify high performers and estimate recruiting demand, directly supporting strategic workforce planning.

• Led end-to-end analytics and ML initiatives to optimize the hiring pipeline, reducing time-to-fill and improving candidate experience through data-driven insights and measurable business impact.

• Queried, cleaned, and transformed large-scale HR datasets using SQL and Apache Spark, accelerating feature engineering, model development, and analytical workflows.

• Designed and executed A/B tests and statistical experiments to evaluate hiring strategies, improving recruiter effectiveness and conversion rates.

• Deployed machine learning models using FastAPI, Docker, and MLflow on AWS (EC2, Lambda, S3), while collaborating with BI and data engineering teams to strengthen data pipelines, automation, and data quality, supported by Git-based version control.

• Developed interactive dashboards using Tableau and Power BI to track key HR metrics like turnover, hiring efficiency and productivity which helped non-technical stakeholders in making data-driven decisions.

• Trained junior analysts and guided them through data preprocessing, choosing the right models, optimization and building dashboards.