Saisravan Prasad Emmadi

Saisravan Prasad Emmadi

AIML Engineer
United States of America

About Me

AI/ML Engineer with 3+ years of experience designing, building, and deploying scalable machine learning, Generative AI, and LLM-powered solutions across enterprise environments. Expertise in Retrieval-Augmented Generatio…

Experience

AI/ML Engineer

Molina Health, USA
Jul 2025 - Present · 1 year 3 months

<p>Developed AI-driven healthcare automation solutions using LLMs, RAG pipelines, and LangChain by analyzing manual workflows, reducing operational effort by 30% across document-heavy clinical processes.<br>
Engineered end-to-end RAG systems by processing 100K+ clinical documents, implementing chunking, embeddings, FAISS/Pinecone vector storage, and hybrid search for faster semantic retrieval.<br>
Improved response quality by applying prompt chaining, function calling, LLM guardrails, and hallucination reduction techniques, increasing healthcare-specific response consistency and accuracy by 20%.<br>
Built scalable backend microservices using FastAPI, REST APIs, OAuth2, JWT, and Swagger/OpenAPI, supporting seamless AI integration across applications handling 1K+ daily requests.<br>
Managed production deployments using Docker, Kubernetes, Azure ML, and Terraform while implementing CI/CD pipelines, maintaining 99% uptime across scalable enterprise AI environments.<br>
Optimized inference pipelines through embedding strategy tuning, context window optimization, and retrieval re-ranking, reducing latency by 25% while improving relevance and response precision.<br>
Implemented MLflow, model monitoring, Prometheus, and Grafana dashboards for experiment tracking, model observability, and production issue detection across deployed AI systems.</p>

AI/ML Engineer

Molina Health, USA
Jul 2025 - Present · 1 year 4 months

Developed AI-driven healthcare automation solutions using LLMs, RAG pipelines, and LangChain by analyzing manual workflows, reducing operational effort by 30% across document-heavy clinical processes., Engineered end-to-end RAG systems by processing 100K+ clinical documents, implementing chunking, embeddings, FAISS/Pinecone vector storage, and hybrid search for faster semantic retrieval., Improved response quality by applying prompt chaining, function calling, LLM guardrails, and hallucination reduction techniques, increasing healthcare-specific response consistency and accuracy by 20%., Built scalable backend microservices using FastAPI, REST APIs, OAuth2, JWT, and Swagger/OpenAPI, supporting seamless AI integration across applications handling 1K+ daily requests., Managed production deployments using Docker, Kubernetes, Azure ML, and Terraform while implementing CI/CD pipelines, maintaining 99% uptime across scalable enterprise AI environments., Optimized inference pipelines through embedding strategy tuning, context window optimization, and retrieval re-ranking, reducing latency by 25% while improving relevance and response precision., Implemented MLflow, model monitoring, Prometheus, and Grafana dashboards for experiment tracking, model observability, and production issue detection across deployed AI systems.

Machine Learning Engineer

Cognizant, INDIA
Feb 2021 - Nov 2023 · 2 years 9 months

<p>Designed and deployed machine learning models for document classification, automation, and NLP workflows using structured and unstructured datasets, improving model prediction accuracy by 25%.<br>
Built scalable ETL and feature engineering pipelines processing 10M+ records using Python, SQL, Airflow, and Spark, improving data quality and operational efficiency significantly.<br>
Enhanced model performance by applying cross-validation, hyperparameter tuning, MLflow experiment tracking, and drift detection, reducing inference time by 30% while maintaining model accuracy.<br>
Developed NLP and document intelligence solutions using transformers, entity extraction, and semantic search techniques, improving precision and recall rates by 20% across enterprise workflows.<br>
Deployed ML models using Docker, AWS SageMaker, Kubernetes, and CI/CD pipelines, supporting 5+ production applications with stable performance and enterprise-grade scalability.<br>
Improved preprocessing workflows using automation, validation frameworks, and data transformation optimization, reducing overall processing time by 25% and improving downstream model reliability.<br>
Collaborated with cross-functional teams and stakeholders to translate business requirements into production ML solutions, delivering 3+ successful POCs with measurable cost savings.</p>

Machine Learning Engineer

Cognizant, INDIA
Feb 2021 - Nov 2023 · 2 years 9 months

Designed and deployed machine learning models for document classification, automation, and NLP workflows using structured and unstructured datasets, improving model prediction accuracy by 25%., Built scalable ETL and feature engineering pipelines processing 10M+ records using Python, SQL, Airflow, and Spark, improving data quality and operational efficiency significantly., Enhanced model performance by applying cross-validation, hyperparameter tuning, MLflow experiment tracking, and drift detection, reducing inference time by 30% while maintaining model accuracy., Developed NLP and document intelligence solutions using transformers, entity extraction, and semantic search techniques, improving precision and recall rates by 20% across enterprise workflows., Deployed ML models using Docker, AWS SageMaker, Kubernetes, and CI/CD pipelines, supporting 5+ production applications with stable performance and enterprise-grade scalability., Improved preprocessing workflows using automation, validation frameworks, and data transformation optimization, reducing overall processing time by 25% and improving downstream model reliability., Collaborated with cross-functional teams and stakeholders to translate business requirements into production ML solutions, delivering 3+ successful POCs with measurable cost savings.

Skills

MySQL PostgreSQL Python A/B Testing Azure Data Governance Data Modeling Data Validation GitHub NumPy Object-Oriented Programming (OOP) Performance Optimization Redis SciPy TensorFlow Unit Testing Deep Learning Docker Elasticsearch Kafka Kubernetes Machine Learning Terraform SQL Bash/Shell Scripting REST API Development FastAPI Microservices Architecture API Gateway gRPC Swagger/OpenAPI OAuth2 JWT RBAC Natural Language Processing (NLP) Generative AI Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Prompt Engineering Prompt Chaining Agentic AI AI Agents Function Calling Tool Calling Model Fine-Tuning Feature Engineering Model Evaluation Cross-Validation Hyperparameter Tuning Drift Detection LangChain LlamaIndex LangGraph Hugging Face Transformers OpenAI API Embeddings Vector Databases Prompt Templates Output Parsers LLM Guardrails Hallucination Reduction RAG Evaluation Frameworks LLM Observability Context Window Optimization Multi-Agent Systems Clinical Document Processing Medical NLP Healthcare Workflow Automation Unstructured Clinical Data Processing Healthcare AI Solutions HIPAA Compliance PII Handling Responsible AI PyTorch Scikit-learn Pandas Pytest Integration Testing Data Cleaning Data Preprocessing Data Transformation ETL Pipelines Large-Scale Data Processing Apache Spark Airflow Databricks Snowflake Stream Processing FAISS Pinecone ChromaDB OpenSearch Hybrid Search Semantic Search BM25 Re-ranking Knowledge Graph Basics AWS SageMaker AWS Lambda ECS EKS Azure ML GCP Vertex AI CI/CD Pipelines MLflow Kubeflow DVC Model Deployment Model Monitoring Model Registry Experiment Tracking Infrastructure as Code (IaC) Serverless Deployment Prometheus Grafana ELK Stack Git Agile/Scrum Production Monitoring Logging Frameworks Incident Handling
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