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I am a GenAI Developer with almost five years of experience designing and deploying production-grade AI systems across healthcare, finance, and enterprise data environments. I specialize in building LLM-driven applications, including RAG chatbots, multi-agent systems, and AI assistants that integrate structured retrieval, accurate grounding, and consistent reasoning. I’m passionate about safety, governance, and reliability. I implement guardrails, policy engines, and vector-based classifiers to keep AI behavior secure and policy-aligned. I’ve built high-quality embedding pipelines using OpenAI, HuggingFace, and SentenceTransformers, designed end-to-end RAG ecosystems with robust retrieval and grounding checks, and tuned models with LoRA, QLoRA, and RLHF to balance accuracy and efficiency. I enjoy delivering full-stack AI solutions with FastAPI and React, containerized on Docker and orchestrated with Kubernetes, and continuously improving experimentation and governance workflows.…I am a GenAI Developer with almost five years of experience designing and deploying production-grade AI systems across healthcare, finance, and enterprise data environments. I specialize in building LLM-driven applications, including RAG chatbots, multi-agent systems, and AI assistants that integrate structured retrieval, accurate grounding, and consistent reasoning. I’m passionate about safety, governance, and reliability. I implement guardrails, policy engines, and vector-based classifiers to keep AI behavior secure and policy-aligned. I’ve built high-quality embedding pipelines using OpenAI, HuggingFace, and SentenceTransformers, designed end-to-end RAG ecosystems with robust retrieval and grounding checks, and tuned models with LoRA, QLoRA, and RLHF to balance accuracy and efficiency. I enjoy delivering full-stack AI solutions with FastAPI and React, containerized on Docker and orchestrated with Kubernetes, and continuously improving experimentation and governance workflows.

Akhila Boddu

AI Engineer, Full Stack Developer, Data Scientist, +3

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I am a GenAI Developer with almost five years of experience designing and deploying production-grade AI systems across healthcare, finance, and enterprise data environments. I specialize in building LLM-driven applications, including RAG chatbots, multi-agent systems, and AI assistants that integrate structured retrieval, accurate grounding, and consistent reasoning. I’m passionate about safety, governance, and reliability. I implement guardrails, policy engines, and vector-based classifiers to keep AI behavior secure and policy-aligned. I’ve built high-quality embedding pipelines using OpenAI, HuggingFace, and SentenceTransformers, designed end-to-end RAG ecosystems with robust retrieval and grounding checks, and tuned models with LoRA, QLoRA, and RLHF to balance accuracy and efficiency. I enjoy delivering full-stack AI solutions with FastAPI and React, containerized on Docker and orchestrated with Kubernetes, and continuously improving experimentation and governance workflows.…I am a GenAI Developer with almost five years of experience designing and deploying production-grade AI systems across healthcare, finance, and enterprise data environments. I specialize in building LLM-driven applications, including RAG chatbots, multi-agent systems, and AI assistants that integrate structured retrieval, accurate grounding, and consistent reasoning. I’m passionate about safety, governance, and reliability. I implement guardrails, policy engines, and vector-based classifiers to keep AI behavior secure and policy-aligned. I’ve built high-quality embedding pipelines using OpenAI, HuggingFace, and SentenceTransformers, designed end-to-end RAG ecosystems with robust retrieval and grounding checks, and tuned models with LoRA, QLoRA, and RLHF to balance accuracy and efficiency. I enjoy delivering full-stack AI solutions with FastAPI and React, containerized on Docker and orchestrated with Kubernetes, and continuously improving experimentation and governance workflows.

Available to hire

I am a GenAI Developer with almost five years of experience designing and deploying production-grade AI systems across healthcare, finance, and enterprise data environments. I specialize in building LLM-driven applications, including RAG chatbots, multi-agent systems, and AI assistants that integrate structured retrieval, accurate grounding, and consistent reasoning.

I’m passionate about safety, governance, and reliability. I implement guardrails, policy engines, and vector-based classifiers to keep AI behavior secure and policy-aligned. I’ve built high-quality embedding pipelines using OpenAI, HuggingFace, and SentenceTransformers, designed end-to-end RAG ecosystems with robust retrieval and grounding checks, and tuned models with LoRA, QLoRA, and RLHF to balance accuracy and efficiency. I enjoy delivering full-stack AI solutions with FastAPI and React, containerized on Docker and orchestrated with Kubernetes, and continuously improving experimentation and governance workflows.

Work Experience

GenAI Developer at Elevance Health

October 1, 2024 - Present

Implemented enterprise-grade LLM safety controls by combining AI, a custom policy rule engine, and vector-based filtering to ensure compliant, secure, and toxicity-reduced outputs in production. Built embedding pipelines using OpenAI Embeddings, domain-tuned HuggingFace models, and Sentence Transformers for efficient semantic search and document retrieval. Developed robust RAG ecosystems by orchestrating retrieval and generation workflows with LangChain and Pinecone for real-time search, and FAISS for scalable offline retrieval. Performed model fine-tuning with LoRA, QLoRA, and RLHF across GPT-4, LLaMA, and T5 to improve accuracy while reducing inference cost. Enhanced retrieval quality with ranking models like ColBERT and DPR, and established retrieval consistency checks to strengthen grounding and reduce hallucinations. Strengthened NLP preprocessing with SpaCy for better upstream context and performed knowledge extraction. Implemented multi-agent AI systems using LangChain Agents an

AI/ML Engineer at Empower

February 1, 2023 - September 1, 2024

Fine-tuned LLMs using Hugging Face Transformers, PyTorch, and PEFT/LoRA; applied to new tasks, reduced training costs, and delivered more accurate summarization and reasoning. Built retrieval-augmented pipelines by orchestrating document flows with LangChain and indexing embeddings through FAISS/Pinecone to ensure grounded, contextually accurate responses. Trained and evaluated models with scikit-learn for baselines, then scaled to deep learning solutions. Created advanced NLP and extraction workflows using TensorFlow and OCR/ONNX models to pull structured information from unstructured text and images. Managed the full ML lifecycle with MLflow to track experiments and SageMaker Pipelines to automate training, validation, and deployment. Automated training and data workflows with Airflow; cleaned and engineered large datasets with Pandas; processed big data with Spark on EMR; deployed self-contained services with Docker and managed scalable inference on Kubernetes.

Associate Data Engineer at Samsung India

June 1, 2020 - July 1, 2022

Built secure AWS cloud environments (VPCs, subnets, security groups) and managed data pipelines with EC2, Auto Scaling, and S3; implemented ETL/ELT with AWS Glue and created streaming ingestion with Kinesis and Lambda. Processed large-scale datasets with Apache Spark on EMR; ensured data quality with schema checks and Great Expectations; maintained data warehouses (Redshift, Snowflake, SQL Server) and various data stores (DynamoDB, Oracle, Cassandra). Wrote CI/CD and Infrastructure-as-Code to ensure consistent deployments; supported monitoring via CloudWatch and logs.

GEN AI Developer at Elevance Health

October 1, 2024 - Present

Implemented enterprise-grade LLM safety controls by combining AI, a custom policy rule engine, and vector-based filtering, ensuring compliant, secure, and toxicity-reduced model outputs for production environments. Built embedding pipelines using OpenAI Embeddings, domain-tuned representations from HuggingFace, and Sentence Transformations for accurate semantic search and document retrieval. Developed robust RAG ecosystems by orchestrating retrieval and generation with LangChain, applying vector indexing through Pinecone for real-time search and FAISS for scalable offline/on-premise retrieval. Executed model finetuning with LoRA, QLoRA, and RLHF across GPT-4, LLaMA, and T5 to improve accuracy while reducing inference cost. Enhanced retrieval quality with ranking models like ColBERT and DPR, establishing retrieval consistency checks to strengthen grounding and reduce hallucinations. Strengthened NLP preprocessing and knowledge extraction with SpaCy and implemented entity extraction, tex