Abhinash R.

Abhinash R.

United States of America
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About Me

Senior Gen AI & ML Engineer with 11+ years of experience delivering scalable AI solutions. Expert in Python, LangChain, and cloud-native architectures (AWS/Azure/GCP). I specialize in building production-ready RAG systems, fine-tuning LLMs, and developing end-to-end ML pipelines—from predictive modeling (XGBoost, Deep Learning) to advanced OCR and anomaly detection. Certified Cloud Architect with a focus on IaC and automated CI/CD for model deployment.

AI, ML & LLM

Naive Bayes Azure ML Studio Artificial Neural Networks (ANN) Convolutional Neural Networks (CNN) Pytorch LangChain Langsmith Agentic AI LangChain AWS Bedrock YAML

Frontend

JavaScript ReactJS Next.js HTML CSS Angular

Backend

Python Java R REST APIs

Database

SQL Vector Databases DynamoDB DynamoDB MySQL Microsoft SQL Server SQLite PostgreSQL

DevOps

Azure DevOps Azure Data Lake Azure Synapse Azure Blob Storage EC2 Api Gateway CI/CD AWS CloudFormation AWS CDK Docker Docker Compose Kubernetes Terraform Jenkins

QA & Testing

Hypothesis Testing

Workflow

Git Github Gitlab Postman Trello Slack Figma

Other

jQuery C C Impala Hive Typescript Statistical Inference Statistical Significance Time Series Analysis Statistical Modelling Experimental Design Supervised Learning Unsupervised Learning Feature Engineering Hyperparameter Tuning Ensemble Methods Decision Trees Random Forest Support Vector Machines (SVM) K-nearest Neighbors (KNN) Classification Algorithms time series forecasting ARIMA SARIMA Prophet Recommendation Systems Scikit Learn Multi-agent Systems Retrieval-augmented Generation (RAG) Autoencoders OCR Model Deployment Tensorflow Keras Tf-idf Long Short-term Memory (LSTM) Named-entity Recognition (NER) Sentiment Analysis Text Classification Machine Translation Text Generation spacy NLTK Hugging Face Prompt Engineering Generative Adversarial Networks (GANs) Chatbots Diffusion Models Knowledge Bases Infrastructure as Code (IaC) Big Data Hadoop Hive HBase Apache Spark Scala Kinesis Sqoop Databricks Amazon Web Services Lambda Bedrock EMR S3 Kinesis Athena IAM Step Functions RDS Teradata VS Code Jupyter Notebook Google Colab Swagger Bash Scripting Conda virtualenv Makefile JSON Nginx Redis Power BI Tableau Excel Google Sheets Notion

Work history

Oracle, Austin, Texas
Gen AI Engineer
2023 - 2026 (3 years)
Remote

  • Led development of Generative AI solutions within Oracle Cloud Infrastructure (OCI), integrating LLMs for intelligent automation, customer engagement, and system diagnostics across enterprise applications.

  • Designed and implemented Retrieval-Augmented Generation (RAG) frameworks leveraging Oracle AI Vector Search, LangChain, and FAISS to enhance document retrieval and enterprise search within Oracle Fusion Cloud modules.

  • Implemented MLOps and LLMOps pipelines with MLflow, Airflow, and OCI Data Science Services to streamline model training, deployment, and continuous governance across multi-cloud environments.

LLMs LangChain FAISS BERT spacyHugging Face Transformers FastAPIFlaskMLFlow AirflowXGBoostLightGBMPytorchKafkaSpark Streaming Delta Lake PysparkSnowflakeGrafanaGitJenkinsJSONYAMLLinux
Paypal, CA(Remote)
Sr. AI/ML Engineer
2021 - 2023 (2 years)
Remote

  • Led design and deployment of Generative Al systems for fraud analytics, transaction monitoring, and conversational support, leveraging LangChain, Vertex AI, and AWS Bedrock.

  • Architected Retrieval-Augmented Generation (RAG) pipelines integrating PayPal's internal data lake with vector databases (Pinecone, FAISS) to support semantic search and fraud case summarization.

  • Deployed RAG-powered virtual assistants and chatbots for customer dispute resolution and fraud escalation workflows, reducing manual triage time by 38%.

LangChain Vertex AI AWS Bedrock Pinecone FAISS BERT spacyHugging Face Transformers MLFlow AirflowSageMaker Promptlayer Neo4jSnowflakeDelta Lake PysparkS3LambdaEC2 DynamoDBApi Gateway Step FunctionsGlue AthenaPythonPandasNumpyXGBoostLightGBMTensorflowPytorchDockerKubernetesTerraformGitGitHub Actions JenkinsJSONYAMLLinux
Verizon Communications, Irving, Texas
AI/ML Engineer
2019 - 2021 (2 years)
Remote

  • Spearheaded the design and deployment of AI models for network fault prediction, churn analysis, and service outage detection, enabling proactive maintenance and customer retention.

  • Built end-to-end machine learning pipelines in AWS SageMaker and GCP Vertex AI, orchestrating model training, validation, and deployment across multiple network monitoring systems.

  • Implemented real-time anomaly detection systems leveraging Kafka streams, Spark Structured Streaming, and PyTorch, reducing false alarms by 28%.

XGBoostLSTMProphet KafkaSpark Structured StreamingPytorchBERT spacyFastAPIDockerKubernetesMLFlow AirflowPysparkAWS GlueBigQuery GrafanaPower BI TableauGitGitHub Actions JenkinsPandasNumpySQLnoSQLPostgreSQLMongoDB
UnitedHealth Group (UHG), North Carolina, USA
Data Scientist
2016 - 2019 (3 years)
Remote

  • Developed predictive ML models (XGBoost, Random Forest, Logistic Regression) for forecasting readmissions, high-cost claimants, and chronic disease progression.

  • Automated data preparation and ML workflows on AWS (S3, EC2, SageMaker, Lambda) for scalable training and deployment of healthcare models.

  • Implemented NLP-based analytics (spaCy, NLTK) to extract ICD/CPT codes, conditions, and treatment patterns from physician notes and unstructured EHR narratives.

XGBoostRandom Forest Logistic Regression AWSS3EC2 SageMaker LambdaspacyNLTKTensorflowKerasTableauPower BI GitJupyter Notebook
JP Morgan Chase, Jersey City, NJ
Python Developer/Data Engineer
2013 - 2016 (3 years)
Remote

  • Collaborated with the Data Engineering and Risk Analytics teams to design scalable data ingestion pipelines consolidating credit card, loan, and customer transaction data across multiple internal systems.

  • Engineered ETL workflows on Google Cloud Platform (GCP) using Cloud Dataflow, Pub/Sub, and BigQuery for real-time data streaming and aggregation from transactional sources.

  • Built Tableau dashboards visualizing key banking KPIs — delinquency trends, utilization ratios, churn indicators, and fraud risk scores.

PythonPysparkGCPBigQuery Dataflow Pub/Sub SQLTeradataApache Airflow DockerTableauPandasNumpyBeautifulsoupMatplotlibSeaborn