Senior Python AI/ML & Generative AI Engineer

SAI KRISH P

Senior Python Developer AI/ML Engineer Generative AI & Cloud Analytics Specialist

Ph: +1-337-***-****

Gmail: ***********@*****.***

PROFESSIONAL SUMMARY

Accomplished Senior Python Developer with 10+ years of experience building enterprise-scale AI/ML platforms, Generative AI solutions, predictive analytics models, and cloud-native data science ecosystems across Healthcare, Pharmaceutical, Manufacturing, Retail, and Technology domains for Fortune 500 clients across the USA.

Strong hands-on expertise developing advanced machine learning and deep learning solutions using Python 3.12, TensorFlow 2.x, PyTorch 2.x, Scikit-Learn, XGBoost, LightGBM, Keras, and Spark MLlib supporting large-scale enterprise predictive analytics and intelligent automation initiatives.

Extensive experience implementing Generative AI and LLM-powered enterprise solutions using Azure OpenAI, OpenAI GPT-4o, LangChain, LlamaIndex, Vector Databases, RAG Architectures, Hugging Face Transformers, Pinecone, and ChromaDB improving intelligent search, workflow automation, and enterprise knowledge management systems.

Expertise architecting cloud-native AI/ML platforms using AWS SageMaker, Azure Machine Learning, Databricks, Snowflake, MLflow, Kubeflow, and MLOps frameworks enabling scalable model training, deployment, monitoring, and enterprise AI lifecycle management.

Strong experience building enterprise data pipelines and distributed analytics frameworks using PySpark 3.5, Apache Spark, Kafka 3.x, Airflow 2.x, Databricks Delta Lake, Snowflake, Hadoop, and dbt supporting high-volume analytical and real-time AI workloads.

Hands-on expertise designing NLP, recommendation engines, fraud detection, predictive maintenance, customer intelligence, and anomaly detection solutions using Deep Learning, NLP, Computer Vision, Time Series Forecasting, and Reinforcement Learning frameworks across multiple industry domains.

Extensive experience implementing scalable APIs, AI microservices, and cloud-native inference platforms using FastAPI, Flask, REST APIs, Docker, Kubernetes, Terraform, and Azure Kubernetes Service (AKS) enabling secure deployment and orchestration of AI-driven enterprise applications.

Strong expertise in enterprise data warehousing, feature engineering, and analytical processing using Snowflake, SQL Server 2022, PostgreSQL, Cosmos DB, MongoDB, Redshift, and Delta Lake including performance tuning, optimization, and high-volume analytical processing.

Experience implementing enterprise governance, security, compliance, and Responsible AI frameworks using Azure AD, RBAC, IAM, Key Vault, encryption standards, ML governance controls, and model explainability techniques ensuring secure and compliant AI operations aligned with HIPAA, SOX, and PCI-DSS standards.

Proven expertise automating AI/ML DevOps and CI/CD workflows using GitHub Actions, Azure DevOps, Jenkins, Docker, Kubernetes, Helm, Terraform, and MLflow pipelines enabling scalable MLOps deployments and enterprise operational efficiency.

Strong experience implementing enterprise monitoring, observability, and model performance tracking solutions using Prometheus, Grafana, ELK Stack, Splunk, Datadog, and Azure Monitor ensuring high availability, proactive issue detection, and continuous AI model optimization.

Excellent experience working in Agile/Scrum delivery environments collaborating with data engineers, architects, product owners, business stakeholders, DevOps teams, and offshore/onshore AI engineering teams delivering scalable enterprise AI modernization initiatives.

TECHNICAL SKILLS

Programming & Scripting

Python 3.12, PySpark 3.5, SQL, R, Scala, Shell Scripting, Java, PowerShell

AI/ML & Generative AI

TensorFlow 2.x, PyTorch 2.x, Scikit-Learn, XGBoost, LightGBM, Keras, MLflow, Hugging Face, OpenAI GPT-4o, LangChain, LlamaIndex, Pinecone, ChromaDB, RAG

Big Data & Data Engineering

Apache Spark 3.5, Hadoop, Kafka 3.x, Airflow 2.x, Databricks, Delta Lake, dbt, Hive, HDFS, Snowflake

Cloud Platforms

AWS SageMaker, Azure Machine Learning, Azure Databricks, Azure Synapse, Azure OpenAI, AWS EMR, AWS Lambda, GCP Vertex AI

Databases & Warehousing

SQL Server 2022, PostgreSQL, MongoDB, Cosmos DB, Snowflake, Redshift, Oracle 19c, Redis

APIs & MLOps

FastAPI, Flask, REST APIs, Docker, Kubernetes, Terraform, Kubeflow, Azure DevOps, GitHub Actions, Jenkins

Visualization & Analytics

Power BI, Tableau, Matplotlib, Seaborn, Plotly, Databricks SQL Analytics

Monitoring & Governance

Prometheus, Grafana, ELK Stack, Splunk, Datadog, Azure Monitor, RBAC, IAM, Key Vault

Methodologies

Agile, Scrum, MLOps, CI/CD, DevSecOps, Responsible AI, SDLC, Model Governance

PROFESSIONAL EXPERIENCE

Client: CVS Health — Scottsdale, AZ

Role: Senior Python Developer / AI Engineer

Duration: October 2025 – Present USA

Architecting enterprise-scale healthcare AI and predictive analytics platforms using Azure Machine Learning, Databricks, PySpark 3.5, TensorFlow 2.x, Snowflake, Azure OpenAI, and Delta Lake supporting clinical analytics, pharmacy intelligence, patient risk scoring, fraud detection, care management optimization, and enterprise healthcare operational modernization initiatives across distributed healthcare ecosystems.

Developed and implemented advanced healthcare AI and Generative AI solutions leveraging OpenAI GPT-4o, LangChain, LlamaIndex, Hugging Face Transformers, Pinecone, ChromaDB, vector databases, and Retrieval-Augmented Generation (RAG) frameworks to enhance clinical document summarization, intelligent search capabilities, patient engagement, healthcare knowledge management, and operational automation across enterprise healthcare environments.

Designed and deployed scalable machine learning and predictive analytics models using Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch, Spark MLlib, and ensemble learning techniques to support patient readmission prediction, disease risk assessment, claims fraud detection, prescription adherence monitoring, and healthcare operational intelligence initiatives.

Built distributed data engineering and feature engineering pipelines utilizing PySpark, Apache Spark, Kafka, Airflow, Databricks Delta Lake, and dbt to process and transform large volumes of clinical, pharmacy, provider, member, and claims data. Established robust MLOps frameworks using MLflow, Kubeflow, Docker, Kubernetes (AKS), Azure DevOps, GitHub Actions, Jenkins, and Terraform to streamline model development, deployment, monitoring, retraining, and governance across cloud-based platforms.

Developed AI-powered recommendation engines, anomaly detection systems, NLP solutions, conversational AI applications, and intelligent automation frameworks using Transformer architectures, deep learning, time-series forecasting, reinforcement learning, and computer vision technologies. Engineered healthcare chatbots, semantic search solutions, Named Entity Recognition (NER) models, and GPT-based applications to improve patient support, clinical search accuracy, and healthcare service automation.

Optimized large-scale analytical workloads and AI inference pipelines through Spark tuning, Snowflake performance optimization, adaptive query execution, partitioning strategies, distributed caching, and GPU-enabled computing environments to improve processing efficiency and reduce inference latency. Implemented Responsible AI, security, and governance controls including Azure AD, RBAC, Key Vault, encryption standards, explainable AI methodologies, bias monitoring, and HIPAA-compliant data protection practices.

Developed cloud-native APIs and microservices using FastAPI, Flask, REST APIs, Docker, and Kubernetes to enable seamless integration between AI models, healthcare applications, and downstream systems. Created executive dashboards and reporting solutions using Power BI, Tableau, Databricks SQL Analytics, Plotly, and Matplotlib for monitoring clinical, operational, and fraud analytics metrics.

Collaborated closely with cross-functional teams including healthcare architects, data engineers, clinical analysts, DevOps, MLOps, and compliance stakeholders while supporting model optimization, production deployments, troubleshooting, technical documentation, and continuous improvement initiatives for mission-critical healthcare AI platforms.

Client: Navistar — Lisle, IL

Role: Senior Python Developer / ML Engineer

Duration: August 2024 – September 2025 USA

Architected enterprise-scale AI, machine learning, and predictive analytics platforms using Azure Machine Learning, Databricks, PySpark, TensorFlow, Snowflake, Kafka, and AWS services to support fleet intelligence, predictive maintenance, manufacturing analytics, supply chain optimization, and connected vehicle ecosystems

Developed advanced Generative AI and LLM-powered solutions leveraging OpenAI GPT-4o, LangChain, LlamaIndex, Hugging Face Transformers, Pinecone, vector databases, and Retrieval-Augmented Generation (RAG) architectures to enhance vehicle diagnostics, maintenance knowledge retrieval, technician assistance, and enterprise decision-making processes.

Built scalable machine learning models using Scikit-Learn, XGBoost, LightGBM, TensorFlow, PyTorch, and Spark MLlib for predictive maintenance, anomaly detection, equipment failure forecasting, operational risk analysis, and fleet performance optimization, resulting in improved uptime and reduced maintenance costs.

Designed and implemented distributed data engineering pipelines using Apache Spark, Kafka, Airflow, Databricks Delta Lake, dbt, and real-time streaming frameworks for processing large-scale IoT sensor, telematics, GPS, manufacturing, and operational datasets.

Established enterprise MLOps frameworks utilizing MLflow, Kubeflow, Docker, Kubernetes (AKS/EKS), Azure DevOps, GitHub Actions, Jenkins, and Terraform to automate model deployment, monitoring, retraining, version control, and cloud-native AI operations.

Developed AI-driven recommendation engines, forecasting models, reinforcement learning solutions, NLP applications, and intelligent automation frameworks to improve route optimization, fuel efficiency, inventory planning, and supply chain performance.

Engineered conversational AI, semantic search, and NLP solutions using BERT, GPT-based architectures, Transformer models, and Named Entity Recognition (NER) to automate maintenance documentation search, service request processing, and technician support workflows.

Optimized AI workloads and analytical platforms through Snowflake performance tuning, Spark optimization, adaptive query execution, distributed caching, and GPU-accelerated computing, significantly reducing training and inference latency.

Implemented enterprise security, governance, and Responsible AI frameworks using Azure AD, IAM, RBAC, Key Vault, encryption standards, explainable AI methodologies, and compliance controls.

Developed cloud-native APIs, microservices, and enterprise dashboards using FastAPI, Flask, Power BI, Tableau, and Databricks SQL Analytics while integrating IoT ecosystems through REST APIs, MQTT, Kafka, and cloud services. Collaborated with cross-functional teams on architecture, deployment, monitoring, production support, and AI-driven digital transformation initiatives.

Client: Merck & Co. — Rahway, NJ

Role: Data Scientist / AI Engineer

Duration: October 2023 – July 2024 USA

Developed enterprise-scale pharmaceutical AI and advanced analytics platforms using Azure Machine Learning, Databricks, PySpark 3.4, TensorFlow 2.x, Snowflake, and Azure OpenAI supporting clinical trial analytics, drug discovery workflows, pharmacovigilance, patient intelligence, and pharmaceutical operational optimization initiatives across distributed healthcare ecosystems.

Built advanced Generative AI and LLM-powered research solutions leveraging OpenAI GPT-4, LangChain, LlamaIndex, Hugging Face Transformers, Pinecone, vector databases, and Retrieval-Augmented Generation (RAG) architectures to enhance clinical document summarization, medical literature search, scientific knowledge management, and research automation workflows.

Designed and deployed scalable machine learning and predictive analytics models using Scikit-Learn, XGBoost, LightGBM, TensorFlow, Spark MLlib, and ensemble learning techniques to improve patient risk prediction, adverse event detection, clinical trial optimization, healthcare analytics, and pharmaceutical demand forecasting.

Engineered distributed data processing and feature engineering pipelines using Apache Spark, PySpark, Kafka, Airflow, Databricks Delta Lake, and dbt for large-scale ingestion, transformation, and analysis of clinical, laboratory, patient, and pharmaceutical datasets.

Implemented enterprise MLOps and AI lifecycle management frameworks utilizing MLflow, Kubeflow, Docker, Kubernetes (AKS), Azure DevOps, GitHub Actions, Jenkins, and Terraform to automate model deployment, monitoring, retraining, versioning, and cloud-native AI operations.

Developed AI-powered NLP, semantic search, and intelligent automation solutions using Transformer architectures, BERT, GPT-based models, Named Entity Recognition (NER), and conversational AI technologies to improve medical coding, clinical search, healthcare support automation, and pharmaceutical research productivity.

Built anomaly detection systems, recommendation engines, and predictive healthcare analytics solutions using deep learning, time-series forecasting, reinforcement learning, and neural network architectures to enhance patient adherence monitoring, operational forecasting, and pharmaceutical intelligence.

Optimized AI workloads and analytical platforms through Snowflake tuning, Spark optimization, adaptive query execution, distributed caching, partitioning strategies, and GPU-accelerated computing, significantly improving processing efficiency and reducing model inference latency.

Contributed extensively to production support, AI model tuning, hyperparameter optimization, deployment troubleshooting, root cause analysis, technical documentation, security remediation, and operational support activities ensuring high availability and operational stability across enterprise pharmaceutical AI platforms.

Client: Xcelvision Technologies — Mumbai

Role: Data Scientist / Machine Learning Engineer

Duration: September 2019 – July 2023 India

Developed enterprise-scale AI/ML and advanced analytics platforms using Python 3.x, TensorFlow 2.x, Scikit-Learn, PySpark, Snowflake, and Databricks supporting predictive analytics, customer intelligence, anomaly detection, recommendation systems, and enterprise operational optimization initiatives across multiple client domains.

Developed advanced machine learning, deep learning, and predictive analytics solutions using XGBoost, LightGBM, Random Forest, CNNs, RNNs, LSTM networks, TensorFlow, and ensemble learning techniques to enhance forecasting accuracy, fraud detection, customer analytics, and enterprise decision-making capabilities.

Designed and implemented scalable data engineering and feature engineering frameworks using Apache Spark, PySpark, Kafka, Hadoop, Airflow, Hive, and Delta Lake for large-scale ingestion, transformation, and processing of structured and semi-structured enterprise datasets.

Built NLP and Generative AI applications leveraging OpenAI GPT models, LangChain, Hugging Face Transformers, BERT, semantic search, Named Entity Recognition (NER), and conversational AI architectures to improve intelligent document processing, enterprise search, chatbot automation, and customer support operations.

Implemented enterprise MLOps and AI lifecycle management frameworks using MLflow, Kubeflow, Docker, Kubernetes, Jenkins, GitHub Actions, and Terraform to automate model deployment, retraining, monitoring, version control, and operationalization across cloud environments.

Developed AI-powered recommendation systems, intelligent automation solutions, computer vision models, reinforcement learning applications, and time-series forecasting frameworks to optimize business operations, customer engagement, and strategic planning initiatives.

Optimized distributed AI workloads and analytics platforms through Spark tuning, query optimization, partitioning strategies, caching mechanisms, GPU acceleration, and distributed computing frameworks, significantly improving model training efficiency and reducing inference latency.

Built cloud-native APIs, inference services, and microservices using FastAPI, Flask, REST APIs, Docker, Kubernetes, and event-driven architectures, enabling seamless integration between AI models and enterprise business applications.

Created interactive analytics dashboards and business intelligence solutions using Power BI, Tableau, Plotly, Matplotlib, and Databricks SQL Analytics to provide real-time visibility into KPIs, operational performance, forecasting trends, and AI-driven insights.

Implemented enterprise security, governance, and Responsible AI practices using IAM, RBAC, encryption standards, Key Vault, explainable AI methodologies, and governance frameworks. Leveraged AWS SageMaker, Azure Machine Learning, Databricks, and Snowflake to deploy scalable AI solutions while implementing observability and monitoring using Prometheus, Grafana, ELK Stack, Splunk, Datadog, and model drift detection frameworks. Collaborated closely with cross-functional teams to support AI strategy, deployment, optimization, and digital transformation initiatives.

Contributed extensively to production support, AI model optimization, hyperparameter tuning, deployment troubleshooting, root cause analysis, technical documentation, operational support, and performance improvement activities ensuring scalability and operational stability across enterprise AI platforms.

Client: SNS Technologies — Pune

Role: Junior Data Scientist / Data Analyst

Duration: July 2016 – August 2019 India

Developed enterprise analytics and machine learning solutions using Python 3.x, Scikit-Learn, SQL, Pandas, NumPy, TensorFlow, and Tableau supporting customer analytics, business forecasting, operational reporting, and predictive modeling initiatives across multiple enterprise client environments.

Developed scalable ETL, data engineering, and analytical processing frameworks using PySpark, Apache Spark, Hadoop, Hive, HDFS, SQL Server, and Airflow to support ingestion, transformation, cleansing, and processing of large-scale structured and semi-structured enterprise datasets for reporting and machine learning initiatives.

Built predictive analytics and machine learning solutions using Regression, Classification, Clustering, Random Forest, Decision Trees, XGBoost, and ensemble learning algorithms to improve forecasting accuracy, customer segmentation, operational efficiency, and business intelligence capabilities.

Designed and developed interactive dashboards and reporting solutions using Power BI, Tableau, Plotly, Matplotlib, Seaborn, and SQL-based reporting frameworks, enabling stakeholders to monitor KPIs, customer trends, forecasting metrics, and operational performance in real time.

Implemented NLP and text analytics solutions using NLTK, SpaCy, BERT, sentiment analysis techniques, and text classification models to enhance document processing, customer feedback analysis, and automated support workflows.

Engineered scalable feature engineering and data preprocessing pipelines using Python, PySpark, Spark SQL, Pandas, and automated validation frameworks to improve data quality, model performance, and analytical processing efficiency across distributed environments.

Developed recommendation systems and anomaly detection frameworks using collaborative filtering, matrix factorization, deep learning, and statistical modeling techniques to enhance customer engagement, operational insights, and fraud detection capabilities.

Leveraged cloud platforms including AWS S3, Azure Machine Learning, Databricks, and Snowflake to support scalable analytics, AI experimentation, and model deployment initiatives. Built RESTful APIs and backend analytical services using Flask, FastAPI, Docker, and cloud-native integration frameworks to enable secure access to machine learning models and enterprise data services.

Optimized ETL and analytical workloads through SQL tuning, indexing, Spark optimization, caching strategies, and distributed computing techniques, significantly improving processing performance and reporting efficiency.

Implemented governance, security, and data quality controls using RBAC, encryption, automated validation, and data profiling frameworks. Developed near real-time analytics solutions using Kafka and Spark Streaming while supporting model evaluation, hyperparameter tuning, A/B testing, production monitoring, and cross-functional Agile delivery initiatives.

Gained strong foundational experience in enterprise SDLC methodologies, machine learning lifecycle management, statistical analysis, distributed data processing, cloud analytics platforms, and AI engineering best practices supporting long-term growth in Data Science and AI/ML domains.

CERTIFICATION

AWS Certified Machine Learning Engineer – Associate

Microsoft Azure AI Fundamentals (AI-900)

Databricks Generative AI Fundamentals

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