manoj kancharla

Skills

Generative AI and NLP: GPT-4, BERT, Llama, OpenAI APIs, Hugging Face, Lang Chain, LLMs, RAG, prompt engineering, embeddings, LLM fine-tuning, agentic orchestration, text classification, document processing, information extraction, text summarization, semantic search, PDF parsing, chunking, partitioning
Machine Learning and Data Science: Python, PyTorch, TensorFlow, Scikit-learn, XGBoost, Random Forest, SVM, Pandas, NumPy, SciPy, NLTK, MLflow, data preprocessing, feature engineering, statistical analysis, model training, model evaluation, model optimization
Cloud, MLOps, and DevOps: AWS, Amazon SageMaker, Amazon S3, Amazon RDS, Azure AI services, Azure Blob Storage, GCP, Vertex AI, Cloud Composer, Google Kubernetes Engine (GKE), Docker, Kubernetes, Terraform, Jenkins, Harness, CI/CD, model deployment, inference endpoints, model registry, model monitoring, logging
Data Platforms and Databases: Databricks, Delta Lake, Snowflake, Apache Airflow, SQL, MySQL,
PostgreSQL, ETL/ELT, data pipelines, data quality, star schema, snowflake schema, OLTP, OLAP
Vector Search and Evaluation: Pinecone, Weaviate, FAISS, vector databases, vector search, hybrid search, Ragas, DeepEval, Promptfoo, hallucination evaluation, answer relevancy, faithfulness, context precision, regression testing
Software Engineering and Collaboration: Java, C/C++, Django, REST APIs, JSON, HTML, CSS, JavaScript, Bash, RabbitMQ, AMQP, Git, Jira, Confluence, Agile, Scrum, SDLC, unit testing, technical documentation.
PROFESSIONAL EXPERIENCE
TIAA | Charlotte, NC
Senior AI/ML Engineer | Feb 2023–Present
Designed and implemented end-to-end AI/ML and Generative AI pipelines supporting conversational AI, RAG, NLP, classification, summarization, information extraction, and LLM fine-tuning use cases.
Developed agentic AI architectures, RAG pipelines, LLM chains, and orchestration workflows using Python, LangChain, Hugging Face, OpenAI technologies, and embeddingbased retrieval.
Built production-grade machine learning solutions using Scikit-learn, TensorFlow, and PyTorch for enterprise-scale applications.
Developed and deployed ML workloads using Google Vertex AI and Amazon SageMaker, including training workflows, model registry processes, scalable inference endpoints, monitoring, and production releases.
Built end-to-end ML pipelines using Databricks, Delta Lake, and MLflow for data preparation, model training, evaluation, deployment, versioning, and rollback.
Developed NLP and document-intelligence solutions for text classification, automated information extraction, PDF/CSV/DOC processing, summarization, and EHR data extraction.
Created LLM fine-tuning and optimization workflows to improve domain alignment, contextual performance, inference efficiency, and GPU resource utilization.
Architected LLM evaluation frameworks using Ragas, DeepEval, and Promptfoo to assess answer relevancy, faithfulness, context precision, F1 scores, and hallucination risk.
Integrated automated LLM regression tests into CI/CD pipelines to maintain quality and reliability as prompts, retrieval logic, and models evolved.
Designed vector data solutions using Pinecone and Weaviate to enable semantic search and high-performance RAG applications.
Applied responsible-AI and AI-governance practices addressing hallucination, bias, fairness, transparency, privacy, security, and model reliability in alignment with NIST principles.
Provided technical leadership and mentoring while collaborating with data engineering, cloud, security, product, and business stakeholders to deliver secure, scalable AI platforms.
Environment: Python, Java, C/C++, PyTorch, TensorFlow, Scikit-learn, LLMs, RAG, Lang Chain,
Hugging Face, OpenAI APIs, Ragas, DeepEval, Promptfoo, Databricks, Delta Lake, MLflow, AWS, SageMaker, Azure, GCP, Vertex AI, Pinecone, Weaviate, Docker, Kubernetes, CI/CD, Git, Jira, NIST.

About

Senior AI/ML Engineer with 9+ years of experience in data science, machine learning, natural language processing (NLP), data engineering, and production AI systems. Recent experience focuses on Generative AI, large language models (LLMs), retrieval-augmented generation (RAG), document intelligence, model evaluation, and scalable MLOps delivery. Hands-on expertise in Python, PyTorch, TensorFlow, Scikit-learn, Hugging Face, Lang Chain, cloud AI platforms, vector databases, CI/CD, and Kubernetes. Experienced in designing secure, governed, and enterprise-grade solutions across financial services, healthcare, and retail environments.