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AI/ML Engineer with 8+ years of experience and 3+ years building Generative AI, LLM, RAG, and agentic AI solutions. Experienced in designing and deploying enterprise-grade LLM applications using GPT-4o/Azure OpenAI, LangChain/LangGraph, CrewAI, AutoGen, and LlamaIndex. Strong background in building scalable RAG systems (vector databases, semantic/hybrid search, knowledge graphs), fine-tuning and serving open-source LLMs (LoRA/PEFT, vLLM), and implementing LLMOps/MLOps with evaluation and monitoring (MLflow, LangSmith/LangFuse, RAGAS/DeepEval). Also has hands-on experience in data engineering pipelines (Databricks, Spark, Kafka) and secure production deployments on Azure/AWS/Kubernetes with governance and guardrails.…AI/ML Engineer with 8+ years of experience and 3+ years building Generative AI, LLM, RAG, and agentic AI solutions. Experienced in designing and deploying enterprise-grade LLM applications using GPT-4o/Azure OpenAI, LangChain/LangGraph, CrewAI, AutoGen, and LlamaIndex. Strong background in building scalable RAG systems (vector databases, semantic/hybrid search, knowledge graphs), fine-tuning and serving open-source LLMs (LoRA/PEFT, vLLM), and implementing LLMOps/MLOps with evaluation and monitoring (MLflow, LangSmith/LangFuse, RAGAS/DeepEval). Also has hands-on experience in data engineering pipelines (Databricks, Spark, Kafka) and secure production deployments on Azure/AWS/Kubernetes with governance and guardrails.

Venkatesh Bathula

AI Engineer, Data Scientist, Software Engineer, +4

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AI/ML Engineer with 8+ years of experience and 3+ years building Generative AI, LLM, RAG, and agentic AI solutions. Experienced in designing and deploying enterprise-grade LLM applications using GPT-4o/Azure OpenAI, LangChain/LangGraph, CrewAI, AutoGen, and LlamaIndex. Strong background in building scalable RAG systems (vector databases, semantic/hybrid search, knowledge graphs), fine-tuning and serving open-source LLMs (LoRA/PEFT, vLLM), and implementing LLMOps/MLOps with evaluation and monitoring (MLflow, LangSmith/LangFuse, RAGAS/DeepEval). Also has hands-on experience in data engineering pipelines (Databricks, Spark, Kafka) and secure production deployments on Azure/AWS/Kubernetes with governance and guardrails.…AI/ML Engineer with 8+ years of experience and 3+ years building Generative AI, LLM, RAG, and agentic AI solutions. Experienced in designing and deploying enterprise-grade LLM applications using GPT-4o/Azure OpenAI, LangChain/LangGraph, CrewAI, AutoGen, and LlamaIndex. Strong background in building scalable RAG systems (vector databases, semantic/hybrid search, knowledge graphs), fine-tuning and serving open-source LLMs (LoRA/PEFT, vLLM), and implementing LLMOps/MLOps with evaluation and monitoring (MLflow, LangSmith/LangFuse, RAGAS/DeepEval). Also has hands-on experience in data engineering pipelines (Databricks, Spark, Kafka) and secure production deployments on Azure/AWS/Kubernetes with governance and guardrails.

Available to hire

AI/ML Engineer with 8+ years of experience and 3+ years building Generative AI, LLM, RAG, and agentic AI solutions. Experienced in designing and deploying enterprise-grade LLM applications using GPT-4o/Azure OpenAI, LangChain/LangGraph, CrewAI, AutoGen, and LlamaIndex.

Strong background in building scalable RAG systems (vector databases, semantic/hybrid search, knowledge graphs), fine-tuning and serving open-source LLMs (LoRA/PEFT, vLLM), and implementing LLMOps/MLOps with evaluation and monitoring (MLflow, LangSmith/LangFuse, RAGAS/DeepEval). Also has hands-on experience in data engineering pipelines (Databricks, Spark, Kafka) and secure production deployments on Azure/AWS/Kubernetes with governance and guardrails.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

Afar

Advanced

English

Advanced

Work Experience

GEN AI Engineer at T Mobile

December 1, 2024 - Present

Led end-to-end deployment of enterprise AI solutions across customer environments, including secure REST API integrations with enterprise systems (CRM, ticketing, auth, knowledge management). Built and configured LLM-based agents using Azure OpenAI with LangGraph and CrewAI to automate customer support and operational workflows. Designed enterprise AI architectures combining LLMs, RAG pipelines, vector databases, REST APIs, and knowledge graphs. Developed scalable retrieval systems using Pinecone, hybrid search, and SPARQL; implemented semantic RAG using LlamaIndex and RDF structuring. Built conversational/stateful agents enabling dynamic multi-step execution. Implemented fine-tuning pipelines using LoRA/PEFT for domain-specific telecommunications language models. Established LLM evaluation frameworks using DeepEval, LangFuse, and RAGAS to reduce hallucinations and improve response quality. Automated deployment using CI/CD, Terraform, Docker, and Kubernetes; monitored production envir

GEN AI Engineer at T Mobile USA

December 1, 2024 - Present

Led end-to-end deployment of enterprise AI solutions across customer environments, ensuring production rollout and adoption. Built secure REST API integrations between AI platforms and enterprise systems (CRM, ticketing, authentication, knowledge management). Designed and deployed AI agents using Azure OpenAI, LangGraph, and CrewAI for automating customer support and operational workflows. Implemented scalable RAG architectures using LlamaIndex, Pinecone, hybrid/semantic search, and RDF-based structuring, including SPARQL/knowledge-graph-driven retrieval. Developed multi-agent orchestration and stateful conversational platforms (CrewAI/LangGraph/AutoGen). Created LLM evaluation frameworks using DeepEval, LangFuse, and RAGAS to improve response quality and reduce hallucinations. Implemented secure, enterprise-grade deployments using CI/CD, Terraform, Docker, and Kubernetes; supported go-live validation and post-deployment monitoring to maintain high availability.

GEN AI ENGINEER at T Mobile, USA

December 1, 2024 - Present

Led end-to-end deployment of enterprise AI solutions across customer environments, supporting onboarding and production rollout. Designed secure REST API integrations between AI platforms and enterprise systems (CRM, ticketing, authentication, knowledge management). Configured and deployed LLM-based AI agents using Azure OpenAI, LangGraph, and CrewAI to automate customer support and operational workflows. Built enterprise architectures combining LLMs, RAG pipelines, vector databases, and REST APIs for secure production deployments. Implemented multi-agent orchestration for autonomous execution of complex telecommunications workflows. Developed retrieval systems with Pinecone and hybrid/semantic search, improving contextual accuracy. Created LLM evaluation and monitoring workflows (DeepEval, LangFuse, RAGAS) to reduce hallucinations and improve quality. Automated deployments using CI/CD, Terraform, Docker, and Kubernetes; troubleshot API/auth/vector database integration issues and suppo

AI/ML Engineer at AIG

January 1, 2023 - November 1, 2024

Implemented production-ready AI and document intelligence solutions for enterprise underwriting use cases. Deployed services using Azure OpenAI with Kubernetes and REST APIs, integrating with document repositories and internal enterprise platforms. Built REST API integrations to connect AI services with enterprise systems and data sources. Collaborated with business stakeholders to translate requirements into deployable solutions including semantic models and structured data relationships. Integrated AI workflows with Snowflake and Azure SQL for real-time document processing and analytics. Participated in production rollout activities including validation, testing, UAT support, monitoring, and SLA issue resolution. Supported SaaS AI implementations by configuring integrations and validating data flows across Stardog and GraphDB environments. Provided technical training and documentation for adoption and ongoing platform usage.

AI/ML Engineer at AIG USA

January 1, 2023 - November 30, 2024

Implemented production-ready AI underwriting and document intelligence solutions for enterprise users. Deployed AI services using Azure OpenAI, Kubernetes, and REST APIs with secure enterprise integrations. Built API integrations connecting AI with document repositories and internal enterprise data platforms. Integrated with Snowflake and Azure SQL using schema design and structured data modeling for real-time document processing and analytics. Troubleshot inference and workflow automation issues within SLA timelines, coordinated production validation/testing, and supported go-live monitoring. Configured SaaS AI integrations and validated data flows across Stardog/GraphDB environments, including technical training and documentation for customer adoption.

AI/ML Engineer at AIG, USA

January 1, 2023 - November 30, 2024

Implemented production-ready AI services for underwriting and document intelligence use cases. Built REST API integrations connecting Azure OpenAI services with document repositories and enterprise data platforms. Collaborated with stakeholders to define requirements and deliver deployable solutions. Integrated AI workflows with Snowflake and Azure SQL for real-time document processing and analytics. Supported end-to-end rollout activities including validation, deployment testing, user acceptance support, and production monitoring. Investigated and resolved inference and integration issues within SLA timelines. Provided technical consulting, troubleshooting guidance, and training/documentation to support customer adoption. Optimized solutions based on feedback, improving response quality and workflow efficiency.

Data Scientist at OPTUM

October 1, 2020 - August 31, 2022

Developed healthcare risk prediction models in Python and TensorFlow, improving patient outcome forecasting accuracy by 32%. Built scalable data engineering pipelines with PySpark and Databricks processing millions of healthcare records. Delivered reporting dashboards using Power BI and Azure SQL. Developed customer segmentation with ML (K-Means) for care management effectiveness. Created NLP analytics using BERT to extract insights from clinical documentation. Built time series forecasting frameworks (including Prophet) and demand planning solutions. Implemented fraud/anomaly detection using XGBoost with feature engineering. Designed real-time integration workflows with Apache Kafka and Spark Streaming, and applied MLOps with Azure Machine Learning to automate deployment and governance.

DATA SCIENTIST at OPTUM, USA

October 1, 2020 - August 31, 2022

Developed healthcare risk prediction and analytics models using Python and TensorFlow, improving patient outcome forecasting accuracy by 32%. Built scalable data engineering pipelines using PySpark and Databricks for millions of healthcare records. Designed enterprise reporting with Power BI and Azure SQL. Implemented customer segmentation with K-Means clustering for targeted care management. Built NLP solutions using BERT for clinical documentation analytics. Automated deployment processes using Azure ML and MLOps to improve governance and reduce release cycles. Developed time-series forecasting frameworks (Prophet) for demand/resource planning. Engineered real-time data integration using Apache Kafka and Spark Streaming. Applied statistical modeling in R for readmission factor analysis and built fraud/anomaly detection using XGBoost to reduce losses from fraudulent claims. Optimized cloud analytics using Azure Data Factory and Data Lake Storage Gen2 and delivered data science solutio

DATA SCIENTIST at ACCENTURE

July 1, 2018 - September 30, 2020

Built predictive analytics models using Python and scikit-learn, improving customer retention forecasting accuracy by 25%. Implemented large-scale data processing pipelines with PySpark and Apache Hadoop. Created executive dashboards using Tableau and SQL. Delivered classification solutions with Random Forest and XGBoost to improve fraud detection precision while reducing false positives. Conducted statistical analysis in R and performed hypothesis testing to identify significant trends. Built NLP applications with NLTK/text mining to extract insights from unstructured feedback. Automated model deployment workflows using Docker and Jenkins for consistent release management. Developed time-series forecasting (ARIMA) for demand planning and optimized analytics with AWS S3 and Amazon SageMaker for scalable training and operations.