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I’m a Senior AI/ML Engineer with 9+ years of experience building production-grade machine learning and NLP systems, and more recently focusing on Generative AI, LLMs, and retrieval-augmented generation (RAG). I design end-to-end pipelines for document intelligence, text classification, summarization, and information extraction—then deploy them in secure, governed, enterprise environments. I enjoy engineering reliable AI outcomes by combining strong model development with rigorous evaluation and MLOps: automated LLM regression testing, monitoring/observability, and scalable training and inference on cloud platforms. I’ve delivered solutions using Python, PyTorch/TensorFlow, and tools like LangChain, Hugging Face, MLflow, Kubernetes, and vector databases to support real business needs across financial services, healthcare, and retail.…I’m a Senior AI/ML Engineer with 9+ years of experience building production-grade machine learning and NLP systems, and more recently focusing on Generative AI, LLMs, and retrieval-augmented generation (RAG). I design end-to-end pipelines for document intelligence, text classification, summarization, and information extraction—then deploy them in secure, governed, enterprise environments. I enjoy engineering reliable AI outcomes by combining strong model development with rigorous evaluation and MLOps: automated LLM regression testing, monitoring/observability, and scalable training and inference on cloud platforms. I’ve delivered solutions using Python, PyTorch/TensorFlow, and tools like LangChain, Hugging Face, MLflow, Kubernetes, and vector databases to support real business needs across financial services, healthcare, and retail.

manoj kancharla

AI Engineer, Data Scientist, AI Developer, +2

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I’m a Senior AI/ML Engineer with 9+ years of experience building production-grade machine learning and NLP systems, and more recently focusing on Generative AI, LLMs, and retrieval-augmented generation (RAG). I design end-to-end pipelines for document intelligence, text classification, summarization, and information extraction—then deploy them in secure, governed, enterprise environments. I enjoy engineering reliable AI outcomes by combining strong model development with rigorous evaluation and MLOps: automated LLM regression testing, monitoring/observability, and scalable training and inference on cloud platforms. I’ve delivered solutions using Python, PyTorch/TensorFlow, and tools like LangChain, Hugging Face, MLflow, Kubernetes, and vector databases to support real business needs across financial services, healthcare, and retail.…I’m a Senior AI/ML Engineer with 9+ years of experience building production-grade machine learning and NLP systems, and more recently focusing on Generative AI, LLMs, and retrieval-augmented generation (RAG). I design end-to-end pipelines for document intelligence, text classification, summarization, and information extraction—then deploy them in secure, governed, enterprise environments. I enjoy engineering reliable AI outcomes by combining strong model development with rigorous evaluation and MLOps: automated LLM regression testing, monitoring/observability, and scalable training and inference on cloud platforms. I’ve delivered solutions using Python, PyTorch/TensorFlow, and tools like LangChain, Hugging Face, MLflow, Kubernetes, and vector databases to support real business needs across financial services, healthcare, and retail.

Available to hire

I’m a Senior AI/ML Engineer with 9+ years of experience building production-grade machine learning and NLP systems, and more recently focusing on Generative AI, LLMs, and retrieval-augmented generation (RAG). I design end-to-end pipelines for document intelligence, text classification, summarization, and information extraction—then deploy them in secure, governed, enterprise environments.

I enjoy engineering reliable AI outcomes by combining strong model development with rigorous evaluation and MLOps: automated LLM regression testing, monitoring/observability, and scalable training and inference on cloud platforms. I’ve delivered solutions using Python, PyTorch/TensorFlow, and tools like LangChain, Hugging Face, MLflow, Kubernetes, and vector databases to support real business needs across financial services, healthcare, and retail.

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Work Experience

Senior AI/ML Engineer at TIAA

February 1, 2023 - Present

Designed and implemented end-to-end AI/ML and Generative AI pipelines for conversational AI, RAG, NLP, classification, summarization, information extraction, and LLM fine-tuning. Built agentic workflows and retrieval pipelines using Python, LangChain, Hugging Face, and OpenAI technologies, including embeddings-based retrieval. Deployed ML workloads on Google Vertex AI and Amazon SageMaker with model registry, scalable inference endpoints, monitoring, and production release processes. Built data preparation, training, evaluation, deployment, versioning, and rollback pipelines using Databricks, Delta Lake, and MLflow. Architected LLM evaluation frameworks using Ragas, DeepEval, and Promptfoo to measure answer relevancy, faithfulness, context precision, hallucination risk, and regression reliability integrated into CI/CD. Developed vector data solutions using Pinecone and Weaviate for semantic and hybrid search, and applied responsible-AI practices aligned with NIST (privacy, security, b

AI/ML Engineer at CBRE

March 1, 2021 - October 1, 2022

Developed machine learning models for customer churn, retention, credit risk, financial behavior analysis, and demand forecasting to support targeted engagement and risk mitigation. Built predictive analytics using Python, PyTorch/TensorFlow, Pandas/NumPy/SciPy and delivered automated ML workflows with Apache Airflow. Created NLP and LLM-based customer-service automation using Hugging Face and OpenAI APIs with prompt engineering and embeddings. Implemented data-labeling and annotation pipelines using Snorkel APIs for customer-interaction datasets and financial text analysis. Leveraged GCP (Vertex AI, Cloud Composer, GKE) for scalable training, deployment, monitoring, batch processing, and real-time inference. Built semantic retrieval and RAG architectures with FAISS and Pinecone. Fine-tuned language models for classification and document processing use cases, and delivered secure REST APIs with authentication and rate limiting. Containerized and deployed models using Docker/Kubernetes

Data Scientist at MetaStar

December 1, 2019 - December 1, 2020

Partnered with healthcare, clinical, data engineering, and operations teams using Agile practices to plan sprints, groom backlogs, and deliver analytics and machine learning outcomes. Designed ETL pipelines and complex SQL transformations to extract, validate, and prepare large healthcare and clinical datasets. Built Python-based analytical services and APIs for interactive healthcare applications and reporting. Conducted statistical analysis to identify correlations, trends, anomalies, and key clinical indicators. Developed ML models using Scikit-learn, XGBoost, Random Forest, and SVM for patient risk stratification, readmission prediction, and outcome prediction. Architected Snowflake data models and feature pipelines integrating EHR, claims, patient, and clinical datasets. Deployed and managed production ML models with Docker and Amazon SageMaker, including monitoring and performance optimization. Implemented hybrid search (SQL retrieval + semantic search) for efficient clinical do

Data Scientist at Walmart

June 1, 2017 - September 1, 2019

Developed Python- and Bash-based data processing and automation for retail analytics, reporting, and operational efficiency. Designed RESTful APIs using Django to capture events and support analytical insights. Built data applications for EDA and trend/pattern/anomaly detection using Pandas/NumPy/Matplotlib. Implemented ETL pipelines extracting from MySQL/PostgreSQL, APIs (REST/JSON), and messaging systems for analytics and reporting. Performed data cleansing, validation, feature preparation, and optimized SQL queries and database models for analytical workloads. Integrated RabbitMQ/AMQP for reliable downstream datasets and automated recurring extraction/transformation/reporting with reusable scripts. Collaborated with Agile teams to gather requirements and deliver scalable data solutions using Git.

Data Scientist at HSBC

June 1, 2016 - January 1, 2017

Built Python-based analytics solutions for customer account analytics, transaction analysis, operational reporting, and decision support. Developed workflows using Python and Django to integrate banking data, and performed EDA, profiling, cleansing, transformations, and validation. Developed statistical and predictive models for customer behavior and transaction patterns. Designed RESTful APIs to integrate analytical models with enterprise banking applications. Automated data preparation, reporting, and monitoring to reduce manual effort, and optimized SQL queries for large dataset extraction and aggregation. Created data visualizations and anomaly detection/data-quality checks to improve reliability of analytical datasets.