Hire Manoj A. - Fine-Tuning Engineer

* Led development of an enterprise internal Generative AI assistant, enabling secure knowledge discovery and operational automation using Vertex AI Generative AI, Open AI GPT-4, and agent-based workflows on GCP. * Designed and deployed RAG pipelines integrating internal documents and business data stored in Cloud Storage and BigQuery, improving response accuracy and contextual relevance by 35%. * Built scalable semantic search and retrieval systems using FAISS, Pinecone, and Vertex AI Vector Search to support LLM-powered enterprise chat applications on GCP. * Developed production-grade AI APIs with Python and FastAPI for document ingestion, embedding generation via Vertex AI embedding models, agent routing, and LLM inference. * Engineered multi-turn conversational agents with persistent memory using LangChain and LangGraph for long-running enterprise use cases on GCP infrastructure. * Optimized prompt engineering and inference parameters — reducing hallucinations and improving response consistency and latency across GCP-deployed LLM workloads. * Deployed and scaled AI services using Docker and Kubernetes on GKE (Google Kubernetes Engine), supporting high-concurrency enterprise workloads on GCP. * Implemented MLOps workflows with MLflow and Vertex AI Pipelines for experiment tracking, model versioning, and production governance on GCP.

* Led the design and automation of ETL and data science pipelines for large-scale healthcare and real-world evidence (RWE) datasets, ensuring data quality, consistency, and HIPAA-compliant processing. * Applied Python, SQL, and PySpark to perform large-scale data preparation, feature engineering, and transformation across claims, provider, prescription, and patient datasets. * Developed predictive and statistical models to analyze treatment outcomes, patient risk patterns, utilization trends, and population-level healthcare metrics. * Built customer segmentation and clustering models to support targeted outreach and marketing initiatives, improving engagement and campaign effectiveness. * Implemented distributed data processing workflows using Apache Spark on AWS EMR to enable high-volume analytics and scalable model development. * Designed and delivered interactive dashboards and reports using Power BI and Tableau to communicate insights to clinical researchers, business stakeholders, and leadership teams. * Implemented data validation, anomaly detection, and quality checks to ensure accuracy and reliability of analytical outputs.

* Developed and deployed ML models for customer segmentation. * Built NLP pipelines using Hugging Face Transformers for sentiment analysis and entity extraction. * Implemented GenAI features using Open AI GPT APIs for automating report generation. * Led the migration of legacy models to cloud-native environments using AWS SageMaker. * Improved estimated customer delivery date (ECDD) accuracy by up to 70% using AI/ML models, which also increased customer satisfaction by 15% and reduced customer calls by 15%. * Built and tested different Ensemble Models such as Bootstrap aggregating, Bagged Decision Trees and Random Forest, Gradient boosting, XGBoost, and AdaBoost to improve accuracy, reduce variance and bias, and improve stability of a model. * Utilized MLLib and Spark machine learning (ML) functionality for machine learning problems such as binary classification, regression, clustering, and collaborative filtering.