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Hi, I’m Rajan Bhargav Souda, a Generative AI Engineer with 5 years of experience designing and deploying advanced AI solutions across healthcare, finance, and enterprise domains. I specialize in building and fine-tuning transformer-based models, LLMs, and multimodal AI systems to turn complex data into actionable insights and measurable impact. Beyond building models, I design scalable ML pipelines and MLOps practices that ensure production-ready deployments, reproducibility, and reliability. I enjoy collaborating with clinicians, data engineers, and product teams to automate workflows, improve decision-making, and drive meaningful business outcomes.…Hi, I’m Rajan Bhargav Souda, a Generative AI Engineer with 5 years of experience designing and deploying advanced AI solutions across healthcare, finance, and enterprise domains. I specialize in building and fine-tuning transformer-based models, LLMs, and multimodal AI systems to turn complex data into actionable insights and measurable impact. Beyond building models, I design scalable ML pipelines and MLOps practices that ensure production-ready deployments, reproducibility, and reliability. I enjoy collaborating with clinicians, data engineers, and product teams to automate workflows, improve decision-making, and drive meaningful business outcomes.

Rajan Bhargav Souda

AI Engineer, Data Scientist, Content Creator, +2

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Hi, I’m Rajan Bhargav Souda, a Generative AI Engineer with 5 years of experience designing and deploying advanced AI solutions across healthcare, finance, and enterprise domains. I specialize in building and fine-tuning transformer-based models, LLMs, and multimodal AI systems to turn complex data into actionable insights and measurable impact. Beyond building models, I design scalable ML pipelines and MLOps practices that ensure production-ready deployments, reproducibility, and reliability. I enjoy collaborating with clinicians, data engineers, and product teams to automate workflows, improve decision-making, and drive meaningful business outcomes.…Hi, I’m Rajan Bhargav Souda, a Generative AI Engineer with 5 years of experience designing and deploying advanced AI solutions across healthcare, finance, and enterprise domains. I specialize in building and fine-tuning transformer-based models, LLMs, and multimodal AI systems to turn complex data into actionable insights and measurable impact. Beyond building models, I design scalable ML pipelines and MLOps practices that ensure production-ready deployments, reproducibility, and reliability. I enjoy collaborating with clinicians, data engineers, and product teams to automate workflows, improve decision-making, and drive meaningful business outcomes.

Available to hire

Hi, I’m Rajan Bhargav Souda, a Generative AI Engineer with 5 years of experience designing and deploying advanced AI solutions across healthcare, finance, and enterprise domains. I specialize in building and fine-tuning transformer-based models, LLMs, and multimodal AI systems to turn complex data into actionable insights and measurable impact.

Beyond building models, I design scalable ML pipelines and MLOps practices that ensure production-ready deployments, reproducibility, and reliability. I enjoy collaborating with clinicians, data engineers, and product teams to automate workflows, improve decision-making, and drive meaningful business outcomes.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

Generative AI Engineer at BJC HealthCare

March 1, 2025 - November 6, 2025

Led the development of an LLM-powered clinical decision support system using PyTorch, TensorFlow, and Hugging Face Transformers to improve diagnostic accuracy by 18% across ICU and cardiology units. Built a retrieval-augmented generation pipeline with Pinecone and FAISS enabling real-time access to patient records and reducing clinician lookup time by 30%. Automated patient query handling and appointment scheduling using BERT and spaCy, cutting administrative workload by 40% and improving patient engagement. Engineered scalable ML pipelines with MLflow, Airflow, and Kubeflow to ensure reproducible training and deployment, reducing operational errors by 25%. Processed over 500 GB of structured/unstructured clinical data monthly with Spark and BigQuery to enable predictive analytics on readmissions and resource utilization. Deployed containerized AI services with Docker/Kubernetes and streamlined CI/CD using Jenkins/GitHub Actions, achieving 99.5% uptime. Fine-tuned LLaMA and T5 with LoR

Generative AI Engineer at Express Scripts

February 1, 2025 - February 1, 2025

Designed GAN and VAE-based generative AI models to automate clinical documentation, reducing manual review workload by 38% and improving data quality across pharmacy operations. Built preprocessing pipelines with Pandas, NumPy, Apache Spark, and Dask to structure 180+ GB monthly prescription/claims data for efficient LLM training. Developed graph-based representations with Neo4j and PyTorch Geometric to uncover patient-medication interactions, improving risk detection accuracy by 20%. Integrated AI-powered recommendation engines into internal pharmacy portals via FastAPI REST APIs, delivering personalized alerts that reduced adverse drug interactions by 15%. Leveraged AWS SageMaker and GCP Vertex AI for distributed training and hyperparameter tuning, accelerating deployment by 40% while ensuring reliability. Applied reinforcement learning with OpenAI Gym and Stable Baselines3 to optimize chronic care medication schedules, increasing adherence by 25%. Established monitoring dashboards w

Machine Learning Engineer at Federal Bank

December 1, 2023 - December 1, 2023

Engineered predictive models to assess loan default risk, improving early detection by 24% and reducing potential financial exposure. Developed anomaly detection systems using Apache Spark and Scikit-Learn to monitor transactions in real time and lower fraudulent activity by 22%. Automated ETL workflows via Apache Airflow to integrate data from banking systems and third-party sources into BigQuery, decreasing data preparation time by 35%. Built streaming analytics pipelines with Kafka and Flink, enabling immediate monitoring of ATM and online transactions and improving operational response speed by 28%. Designed customer behavior prediction models with XGBoost and LightGBM, supporting retention and engagement strategies that increased loyalty metrics by 18% over six months. Deployed ML models and analytics pipelines using Docker and Kubernetes, ensuring robust scaling and 99% uptime for mission-critical banking operations. Created executive dashboards in Tableau and Power BI to visuali

Data Scientist at Goldman Sachs (Wipro)

January 31, 2023 - January 31, 2023

Built scalable data pipelines with Snowflake and Google BigQuery; developed predictive analytics to forecast client churn and market trends, improving portfolio decision accuracy by 22%. Engineered NLP-driven workflows to extract insights from regulatory filings and research reports, accelerating compliance reviews by 28%. Implemented real-time data streaming using Kafka and Google Cloud Pub/Sub, reducing anomaly response time by 25%. Constructed relationship and transaction graphs with Neo4j to uncover hidden client interactions, enhancing fraud detection and cross-sell opportunities by 18%. Deployed containerized ML solutions via Docker and Kubernetes for seamless production integration. Managed model lifecycle and performance monitoring using Weights & Biases and Prometheus to maintain 99% production uptime.

Data Scientist at Goldman Sachs (Wipro)

January 1, 2023 - January 1, 2023

Built scalable data pipelines with Snowflake and Google BigQuery, centralizing cross-functional financial datasets and reducing reporting latency by 35%. Developed predictive analytics models with TensorFlow and PyTorch to forecast client churn and market trends, improving portfolio decision accuracy by 22%. Engineered NLP-driven workflows with spaCy and Transformers to extract insights from regulatory filings and research reports, accelerating compliance reviews by 28%. Implemented real-time data streaming solutions using Kafka and Google Cloud Pub/Sub, enabling instant monitoring of trading events and reducing anomaly response time by 25%. Constructed relationship and transaction graphs using Neo4j, revealing hidden client interactions and transaction patterns that enhanced fraud detection and cross-sell opportunities by 18%. Designed executive dashboards with Plotly Dash and Tableau to visualize KPIs, trading metrics, and risk analytics, enabling faster strategic decisions for senio

Data Scientist at Google

September 30, 2022 - September 30, 2022

Developed Transformer-based NLP models with TensorFlow and PyTorch to improve Google Assistant query understanding, boosting user satisfaction by 25%. Automated extraction and normalization of datasets using Apache Airflow, Dataflow, and BigQuery, reducing preprocessing time by 40%. Enhanced image recognition in Google Photos by integrating CLIP and OpenCV, increasing visual search accuracy by 22%. Implemented real-time analytics with Kafka and Google Cloud Pub/Sub, reducing latency in ads and personalization by 30%. Built RL pipelines with RLlib and PyTorch for ad optimization, increasing CTR prediction accuracy by 18%. Created summarization and content recommendation models with Hugging Face Transformers for Google News, delivering a 15% uplift in engagement. Deployed containerized LLM and CV models with Docker/Kubernetes for production, achieving 99% uptime across services.

Data Scientist at Google

September 1, 2022 - September 1, 2022

Developed Transformer-based NLP models with TensorFlow and PyTorch, improving Google Assistant query understanding and boosting user satisfaction metrics by 25%. Automated extraction and normalization of structured and unstructured datasets using Apache Airflow, Dataflow, and BigQuery, reducing preprocessing time by 40%. Enhanced image recognition accuracy in Google Photos by integrating CLIP and OpenCV into computer vision pipelines, increasing visual search precision by 22%. Implemented real-time analytics using Kafka and Google Cloud Pub/Sub, decreasing ad delivery and search personalization latency by 30% and supporting instantaneous insights. Engineered reinforcement learning pipelines with RLlib and PyTorch for ad optimization, increasing click-through prediction accuracy by 18%. Built summarization and content recommendation models with Hugging Face Transformers, driving a 15% uplift in user engagement on Google News. Designed cross-platform dashboards using Looker and Tableau t