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Teja Reddy - AI Engineer

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TEJA REDDY AI/ML Engineer Email: [email protected] | Phone: +1 (940) 407-8601 | LinkedIn PROFESSIONAL SUMMARY AI/ML Engineer and a Senior Data Scientist with 10+ years of experience designing, deploying, and optimizing AI-driven workflow automation systems across finance, health, and insurance domains. Strong expertise in building LLM-based RAG and agentic AI systems using LangGraph, LangChain, Azure OpenAI, and Azure AI Search for enterprise-scale intelligent automation. Proficient in Python with hands-on expertise across PyTorch, TensorFlow, Scikit-learn, FastAPI, and modern AI/ML frameworks for production-grade application development. Strong background in data science & analytics, leveraging Pandas, NumPy, Scikit-learn, Dask, Spark, and BigQuery to perform advanced data wrangling, feature engineering, and large-scale distributed processing. Hands-on experience with LangGraph, ReAct agents, Model Context Protocol (MCP), tool calling, and multi-agent orchestration for building extensible agentic AI workflows. Experienced in embedding Generative AI capabilities into data and analytics workflows, integrating LLM frameworks (OpenAI, Anthropic, Hugging Face) to drive automated data summarization and insight generation. Built advanced RAG and hybrid retrieval pipelines combining vector embeddings, keyword/semantic search, metadata filtering, ranking, and permission-aware retrieval. Expert in Generative AI and multimodal systems, leveraging text, image, and audio models (Whisper, Stable Diffusion) for contextual understanding and automation. Skilled in rapid prototyping and POC development using Builder.io, Databricks, PySpark, and Python to validate AI-driven product concepts and accelerate innovation cycles. Contributed to internal guidelines for responsible and scalable AI adoption, including prompt design standards, governance, and guardrail integration for enterprise LLM deployments. Designed multi-LLM orchestration and multi-agent architectures using Azure OpenAI, Azure AI Foundry, and LangGraph, enabling configurable access to GPT, Claude, and Grok with intent-aware routing, specialist agents, targeted RAG, response synthesis, and agent memory through a unified enterprise AI platform. Developed reusable Skills, Knowledge Base, and scheduled AI workflow frameworks supporting user-managed knowledge, agent capabilities, and autonomous task execution. Skilled in cloud & DevOps, with expertise in AWS SageMaker, Lambda, ECS, Azure Cognitive Services, Docker, Kubernetes, Helm, and Terraform to deliver scalable, secure, and production-ready AI/ML deployments. Expert in relational and NoSQL databases including PostgreSQL, MySQL, MongoDB, Redis, and Elasticsearch, with experience in query optimization, data modeling, and high-performance pipelines. Expertise in Generative AI models (GPT-3/4/5, LLaMA 2, Stable Diffusion, DALL E, Whisper) and NLP frameworks (Hugging Face Transformers, spaCy, NLTK) for developing advanced conversational and text-generation solutions. Implemented AI observability and evaluation using Azure Monitor, Application Insights, OpenTelemetry, Prometheus, Grafana, tracing, feedback analytics, and automated testing. Proven ability to deploy production-grade AI models and backend Python microservices (FastAPI, Flask) integrated with Airflow, Spark, and Kafka pipelines, delivering compliant automation for loan and risk workflows. Developed multimodal AI solutions combining text, image, and speech processing for advanced real-world applications, utilizing Azure Speech, Stable Diffusion, Whisper, and custom LLMs to deliver seamless cross-modal intelligence. Designed and implemented Agent-to-Agent (A2A) communication protocols enabling autonomous collaboration, dynamic task delegation, and seamless coordination among LLM-based agents. Expertise in cloud-native AI deployments using AWS SageMaker, Azure Cognitive Services, and GCP Vertex AI, integrating CI/CD automation pipelines for secure, scalable, and efficient production workflows. Implemented MLOps practices including CI/CD (GitHub Actions, Jenkins, GitLab CI), containerization (Docker, Kubernetes, Helm), and experiment tracking (MLflow, Weights & Biases). Delivered predictive analytics and anomaly detection systems that significantly reduced fraud and financial risks. Developed interactive dashboards and visualization tools with Tableau, Power BI, Matplotlib, Plotly, and Seaborn to track AI performance, enabling real-time monitoring and data-driven decision-making. Led cross-functional teams and mentored developers in delivering AI solutions while fostering collaboration and technical excellence. Ensured regulatory compliance with GDPR and HIPAA throughout deployment and operations. Adept at translating complex business requirements into scalable AI solutions, collaborating with executives, data scientists, and engineering teams to drive measurable business value. TECHNICAL SKILLS Programming Languages Python (Advanced), SQL, Scala, JavaScript (Basic), Shell Scripting AI / Machine Learning Frameworks TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, LightGBM, CatBoost, ONNX, Hugging Face Transformers Generative AI & LLM Ecosystem GPT-4/5, Claude, Grok, Gemini, LLaMA, Azure OpenAI, Azure AI Foundry, LangChain, LangGraph, ReAct Agents, Model Context Protocol (MCP), OpenAI API, Anthropic API, Guardrails AI, PromptLayer, LangSmith, LoRA/QLoRA, PEFT, AWS Bedrock Agentic AI & Orchestration LangGraph, ReAct Agents, Multi-Agent Orchestration, Tool Calling, MCP Servers/Tools, Skills, AI Agents, LLM Routing, Agent Memory, Scheduled AI Workflows, Human-in-the-Loop (HITL) RAG & Vector Databases AI Search, Pinecone, FAISS, Weaviate, ChromaDB, ElasticSearch, Vector Embeddings, Retrieval Evaluation (BLEU, ROUGE, F1) Data Science & Analytics Pandas, NumPy, SciPy, Dask, Spark MLlib, Databricks, Delta Lake, Feature Engineering, SHAP, LIME, Evidently AI (Drift Detection), Time-Series Forecasting (Prophet), Clustering, PCA Cloud & MLOps Platforms AWS (SageMaker, Bedrock, Lambda, ECS, S3, Redshift), Azure (OpenAI, AI Foundry, AI Search, Container Apps, ACR, Static Web Apps, Web PubSub, Document Intelligence, Blob Storage, Service Bus, Application Insights, Monitor), GCP (Vertex AI, BigQuery), MLflow, Weights & Biases, Kubeflow, CI/CD (GitHub Actions, Jenkins, GitLab CI), Docker, Kubernetes, Helm, Terraform Data Engineering & Pipelines Apache Spark, PySpark, Airflow, dbt, Kafka, Delta Lake, ETL/ELT Design, Data Validation, Feature Store (Feast) Visualization & Monitoring Tableau, Power BI, Plotly, Matplotlib, Seaborn, Azure Monitor, Application Insights, Grafana, Prometheus, Streamlit, Dash Databases Relational (PostgreSQL, MySQL) and Non-relational (MongoDB, Elasticsearch, Redis) Snowflake, Hive, Oracle 11g. Security & Privacy HIPAA, SOC2, GDPR compliance in AI/ML workflows Big-Data Framework Hadoop Ecosystem 2.X (HDFS, MapReduce, Hbase 0.9), Spark Framework 2.X (Scala 2.X, Spark SQL, Pyspark, Spark, Mllib) AI Infrastructure and Platforms NVIDIA CUDA (A100, T4), TensorRT, Mixed-Precision Training, Model Quantization, GPU Workload Profiling PROFESSIONAL EXPERIENCE Client: Accelerant, Sandy Springs, GA Aug 2025 - Present Role: Gen AI Engineer Responsibilities: Designed and developed Accelerant AI Hub, a centralized enterprise Generative AI platform and marketplace providing employees unified access to AI assistants, multiple LLMs, enterprise knowledge, AI agents, skills, connectors, MCP integrations, scheduled tasks, and internally hosted AI applications through a single platform. Developed the core agentic Retrieval-Augmented Generation (RAG) architecture using LangGraph, Azure OpenAI, and Azure AI Search, enabling grounded and context-aware responses across enterprise knowledge with source citations, session context, and conversational follow-up support. Implemented advanced hybrid retrieval combining vector embeddings, keyword search, semantic retrieval, metadata filtering, query expansion, ranking, and permission-aware search, improving the relevance and grounding of responses across distributed enterprise knowledge sources. Built LangGraph-based ReAct agent workflows with dynamic tool calling, enabling LLMs to solve user requests, invoke enterprise tools, perform iterative retrieval and generate context-aware responses for complex multi-step interactions. Developed a multi-LLM architecture using Azure OpenAI and Azure AI Foundry, providing configurable model selection that allows users to access and select preferred models including GPT, Claude, Grok through a unified AI Hub experience. Designed an advanced LLM orchestration and multi-agent architecture using LangGraph, enabling intent-aware routing across domain-specialist agents, MCP tools, connectors, and targeted RAG, with parallel agent execution, response synthesis, conversational context, and memory-assisted dispatch for complex enterprise queries. Implemented Model Context Protocol (MCP) capabilities within the AI Hub, enabling users to connect MCP servers, tools, and contextual resources and extend agent capabilities for enterprise and external-system workflows. Developed a reusable Skills framework supporting global and personal skills, AI-assisted skill generation and refinement, skill sharing, file-backed context, administrative promotion, and reusable execution patterns for specialized agentic AI workflows. Built an integrated Knowledge Base supporting document uploads, folder organization, sharing, indexing, re-indexing, and user/group-level permission-aware retrieval, enabling user-managed enterprise knowledge to be securely incorporated into RAG and agentic AI workflows. Developed the AI Hub Apps Marketplace, centralizing access to internally developed and externally hosted AI applications including Replit, Vercel and Vibe- coded claude applications, with capabilities for application discovery, categorization, favorites, usage tracking, and administrative approval workflows. Implemented enterprise-grade authentication and authorization using Microsoft Entra ID, OAuth 2.0, JWT validation, RBAC, and Microsoft Graph On-Behalf-Of (OBO) flows, enabling secure SSO and delegated access to organizational resources based on authenticated-user permissions. Designed permission-aware enterprise retrieval and security controls using Entra user/group identities, metadata-driven security trimming, session ownership validation, source-level authorization, query sanitization, prompt-injection protection, and access-aware retrieval to ensure users and AI agents access only authorized content. Developed scalable backend services using Python and FastAPI, integrated through MuleSoft as the enterprise API gateway layer, to securely expose and govern AI Hub services supporting agent orchestration, RAG, LLM interactions, streaming responses, enterprise integrations, Knowledge Base, skills, scheduled workflows, feedback, and platform administration. Contributed across the React-based frontend and Azure deployment architecture, implementing AI Hub user experience, model selection, agents, Knowledge Base, authentication, session history, file uploads, and streaming responses while deploying services using Docker, Azure Container Apps, Azure Container Registry, Azure Static Web Apps, Azure Web PubSub, and GitHub Actions. Contributed to the architecture of a hybrid local/cloud LLM inference strategy, designing policy-aware model routing based on data sensitivity, model quality, latency, capacity, and cost, with governed fallback between locally hosted open-weight models and approved cloud LLMs. Implemented production observability, evaluation, and AI governance using Azure Monitor, Application Insights, OpenTelemetry, structured logging, tracing, feedback analytics, and automated testing, while contributing to patterns for tool authorization, auditability and controlled agent actions. Environment: Python, FastAPI, React, LangGraph, LangChain, Azure OpenAI, Azure AI Foundry, Azure AI Search, GPT, Claude, Model Context Protocol (MCP), Microsoft Graph API, Microsoft Entra ID Azure Document Intelligence, Azure Blob Storage, Azure Service Bus, Azure Web PubSub, Azure Container Apps, Azure Container Registry, Azure Static Web Apps, Docker, GitHub Actions, Azure Monitor, Application Insights, OpenTelemetry, PostgreSQL, Redis, Replit, Vercel, REST APIs, Linux. Client: PennyMac, Westlake Village, CA Dec 2022 July 2025 Role: Gen AI/ML Engineer Responsibilities: Designed and deployed LLM-powered workflow automation systems for loan origination and servicing using GPT-4, LangChain, achieving 50% faster case processing and reducing manual review errors by 35%. Built retrieval-augmented generation (RAG) pipelines using Elastic Search and Pinecone, enhancing the precision of financial insights and improving loan eligibility predictions and response accuracy by 30%. Explored integration of LLMs into Databricks workflows for semantic data discovery and contextual summarization across risk and loan analytics, while developing GenAI prototypes using Python microservices to demonstrate AI-powered decision support capabilities. Developed and fine-tuned ML models for financial risk assessment, loan eligibility prediction, and anomaly detection using TensorFlow, PyTorch, and Scikit-learn, reducing default risk by 15%. Built and deployed scalable ML APIs and backend microservices using FastAPI and Flask, integrated with Airflow and Kafka to support real-time loan automation and fraud detection workflows. Developed intelligent anomaly detection systems to identify and flag fraudulent transactions in real time, reducing false positives and strengthening overall fraud prevention strategies. Enhanced model interpretability using SHAP and LIME to improve transparency, support regulatory compliance, and build stakeholder confidence in AI-powered financial risk assessment models. Developed internal RAG evaluation metrics (BLEU, ROUGE, precision/recall) to evaluate LLM output quality and support reliable Generative AI deployments. Built end-to-end CI/CD pipelines for AI models and data workflows using GitHub Actions, Jenkins, Docker, and Kubernetes, enabling scalable, secure, and automated production deployments. Integrated Guardrails AI for LLM response validation and PromptLayer for prompt experimentation and performance tracking, supporting responsible AI deployment within regulated finance environments. Partnered with Product, Data Engineering, Finance, Compliance, and Risk teams to translate business requirements into functional GenAI POCs and production-oriented ML solutions while adhering to regulatory and enterprise compliance standards. Cut model training time by 40% through GPU optimization, streamlined data pipelines, and advanced hyperparameter tuning, accelerating experimentation while improving model accuracy and convergence. Environment: Python, TensorFlow, Py Torch, Scikit-learn, Hugging Face Transformers, Lang Chain, FAISS, Pinecone, Elastic Search, Kafka, Spark, Airflow, ML flow, AWS SageMaker, Docker, Evidently AI, Prometheus, Grafana, Keras, Fast API, Flask, Django, PostgreSQL, NVIDIA A100 GPUs, MongoDB, Redis, AWS (Lambda, S3, ECS, EC2), GCP Vertex AI, Kubernetes, Helm, Terraform, GitHub Actions, Jenkins, GitLab CI, Weights & Biases, Power BI, Matplotlib, Seaborn, Plotly, Linux, VS Code, PyCharm, Jupyter Notebook. Client: Elevance Health, Indianapolis, IN July 2020 - Nov 2022 Role: Machine Learning Engineer Responsibilities: Developed and fine-tuned supervised and unsupervised ML models including classification, regression, and clustering using Scikit-learn, TensorFlow, and PyTorch to generate actionable insights for healthcare analytics. Built and deployed deep learning models (CNNs, RNNs, Transformers) for medical imaging and patient record NLP, improving diagnostic accuracy by 18% and reducing manual review time by 30%. Led a cross-functional prototype initiative integrating Generative AI with Databricks and Azure ML for automated document summarization and insight generation from clinical data. Built advanced NLP pipelines using spaCy, NLTK, and Hugging Face Transformers for sentiment analysis, clinical document summarization, and Named Entity Recognition (NER) from medical text. Designed and automated end-to-end ML pipelines using MLflow and Airflow, enabling reproducible training, validation, and deployment workflows with enhanced traceability and version control. Containerized ML models using Docker and deployed them on Kubernetes, AWS SageMaker, ensuring high scalability, robust security, and enterprise-grade compliance across environments. Implemented comprehensive model monitoring using Prometheus, Grafana, and Evidently AI to detect model drift, latency, and bias in production. Collaborated with clinicians, product managers, and DevOps teams to integrate ML solutions into enterprise healthcare platforms, ensuring seamless deployment and strict adherence to HIPAA compliance standards. Environment: Python (NumPy, Pandas, Scikit-learn, TensorFlow, Py Torch, Keras, Hugging Face, spaCy, NLTK, OpenCV), SQL, Spark, ML flow, Airflow, Docker, Kubernetes, AWS SageMaker, Azure ML, FAISS, Pinecone, Prometheus, Grafana, GitHub Actions, Jenkins, Terraform, SOC2/GDPR/HIPAA-compliant environments. Client: State of PA, Harrisburg, PA. Feb 2018 - June 2020 Role: Data Scientist Responsibilities: Designed and implemented Azure Database Migration Service (DMS) strategies to migrate on-premises SQL Server and Oracle databases to Azure SQL and Synapse Analytics, ensuring secure and reliable data availability for analytics and ML use cases. Built and maintained scalable ETL/ELT pipelines using Azure Data Factory (ADF), Databricks, and PySpark to efficiently ingest, transform, and prepare large datasets for machine learning and advanced analytics. Performed data profiling, cleansing, and feature engineering using PySpark, Pandas, and SQL to generate ML-ready datasets that powered predictive modeling, reporting, and analytical dashboards. Developed and validated statistical models and ML prototypes to analyze healthcare and public-sector datasets, driving data-informed decisions and enhancing forecasting accuracy and policy insights. Integrated Azure ML into Databricks workflows, automating model retraining, validation, and deployment pipelines to streamline predictive analytics processes. Leveraged Lakehouse architecture (Delta Lake) to unify structured, semi-structured, and unstructured data into a consistent layer, enabling feature engineering, ML training datasets, and BI workloads. Engineered Spark Structured Streaming pipelines with Kafka to process real-time events, supporting low-latency feature generation and continuous ML model updates for dynamic analytics. Strengthened data security, governance, and monitoring using RBAC, Managed Identities, Azure Key Vault, encryption, Azure Monitor, Log Analytics, and Application Insights, improving access control and operational reliability across ML and data workflows. Environment: Microsoft Azure (SQL, Synapse Analytics, Data Factory, Databricks, Data Lake Storage, Key Vault, Azure ML, Azure Cognitive Services, DevOps), Snowflake, Apache Spark, Delta Lake, Kafka, Py Spark, Pandas, NumPy, Scikit-learn, SQL, Power BI, Matplotlib, Seaborn, Git. Client: BNSF Railway | Fort Worth, Texas. Dec 2016 Jan 2018 Role: Data Engineer Responsibilities: Engineered scalable ETL pipelines and data workflows using Python, PySpark, and SQL to deliver near real-time analytics and operational insights for railway performance optimization. Built and optimized batch and streaming data pipelines using Apache Spark and AWS S3, improving data availability, reliability, and accessibility for analytics and reporting workloads. Developed scalable data lake solutions on AWS using Parquet, Avro, and JSON formats to optimize data storage, retrieval efficiency, and query performance across analytics workloads. Integrated Kafka and AWS Lambda into event-driven ingestion frameworks, enabling real-time data processing and monitoring of railway operations. Automated incremental data ingestion workflows with built-in validation, monitoring, and error-handling logic, improving reliability, consistency, and traceability across diverse data sources. Collaborated with data science teams to deliver AI/ML-ready datasets supporting predictive maintenance, trend analysis, and anomaly detection, including Spark MLlib pipelines for analyzing sensor data. Implemented CI/CD and containerized data services using Git, Jenkins, Docker, and Kubernetes, while applying IAM, encryption, and role-based access controls to support scalable and secure production data pipelines. Environment: Python, Py Spark, Apache Spark, SQL, Hadoop, AWS (S3, Lambda, EMR, Redshift), Kafka, Docker, Kubernetes, Jenkins, Git, Tableau, PostgreSQL, Hive, Parquet, Avro, JSON, Linux. Client: Ford, India Aug 2014 - Aug 2016 Role: Data Analyst Responsibilities: Collected, cleaned, and transformed large datasets using Python (Pandas, NumPy) and SQL to support sales, operations, and marketing analytics with accurate, insight-driven reporting. Conducted exploratory data analysis (EDA) using Python and SQL to uncover key sales, demand, and customer behavior patterns that informed forecasting and business strategy. Automated monthly forecasting pipeline using Prophet and Scikit-learn, improving planning accuracy by 20%. Designed and delivered interactive dashboards and analytical reports using Power BI and Python (Matplotlib, Seaborn), providing actionable insights and data-driven visibility to business stakeholders. Built and deployed predictive models that increased sales forecast accuracy by 22% and enabled $5M in optimized, data-driven inventory planning decisions. Automated reporting pipelines with Python and SQL, reducing manual reporting time by 70% and improving reporting accuracy. Partnered with cross-functional teams to translate business requirements into actionable insights and interactive visual reports using Power BI for informed decision-making. Developed NLP prototypes using SpaCy and NLTK to analyze unstructured customer feedback, extracting sentiment trends and thematic insights to guide product strategy. Collaborated with engineering teams to Integrate AI/ML outputs into existing data workflows, enhancing automation, scalability, and business adoption of analytics solutions. Optimized SQL queries and Python scripts, improving data pipeline throughput and reducing processing overhead. Documented analytics workflows in Jupyter Notebooks and presented insights through Power BI and Excel dashboards, ensuring accessibility and clarity for both technical and non-technical stakeholders. Environment: Python (Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn), SQL (MySQL, PostgreSQL), Power BI, Excel, NLP (SpaCy, NLTK), Jupyter Notebook, Git, Linux EDUCATION Bachelor of Computer Applications - IIMC, INDIA Keywords: continuous integration continuous deployment artificial intelligence machine learning business intelligence sthree California Georgia Idaho Pennsylvania