Senior AI/ML Engineer
AI-native engineer
Available for hire
Years of
experience
10+ years
Experience
level
Senior
Available
for
Full-time, Part-time, Contract, Freelance
Available from
23 Jun 2026
I’m a Senior AI/ML Engineer with 12+ years of experience building scalable AI, cloud, and data platforms across healthcare, fintech, SaaS, and analytics. What makes me unique is my ability to bridge deep technical execution with real business impact—I don’t just prototype AI systems, I deliver production-ready solutions that solve measurable problems.
I’ve designed and deployed GenAI, RAG, and agentic AI systems using LLMs, LangChain, vector databases, and cloud-native architectures on Azure and AWS. I’m also highly hands-on with modern AI development workflows and tools like Cursor, Claude Code, Copilot, and ChatGPT to accelerate development and innovation.
I enjoy fast-moving teams, challenging engineering problems, and roles where I can lead architecture, mentor engineers, and help companies turn AI into real products and business value. I’m looking for senior AI/ML engineering or AI platform roles focused on GenAI, applied ML, intelligent automation, and scalable enterprise AI systems.
Skills
Languages
Employment History
Senior AI Engineer at
Elevance Systems
2025
-
2026
Leading end-to-end design, development, and deployment of AI/ML and Generative AI solutions in the US Healthcare domain. Responsible for client engagement (Franciscan Health), solution architecture, delivery execution, and team leadership.
● Led AI/ML and Generative AI initiatives for healthcare automation, claims processing, and clinical workflow optimization, delivering scalable enterprise-grade solutions on Microsoft Azure..
● Architected and deployed production-ready AI applications using FastAPI, Docker, GitHub Actions, and Azure Cloud services while managing full SDLC from requirements gathering through deployment and optimization.
● Developed “Doc Assist,” an AI-powered medical conversation platform utilizing Whisper, PyAnnote, Azure OpenAI, and FHIR APIs to automate clinical documentation and reduce manual effort by over 60%.
● Built an AI-driven healthcare IVR voice bot integrating Azure Speech Services, LangChain, and LLM orchestration to automate patient interactions and reduce call center workload by 40%.
● Designed and implemented Agentic AI and RAG-based systems using LangGraph, FAISS, and Pinecone for intelligent analytics, root-cause analysis, and executive reporting across healthcare denial management platforms.
● Created an advanced denial reduction application leveraging XGBoost, Random Forest, NLP models, and LLM-based recommendation engines to predict and prevent insurance claim denials before submission.
● Own full lifecycle: Requirement → Architecture → Development → Deployment → Optimization.
Senior Machine Learning Engineer at
Vistendo Inc
2021
-
2025
● Supervised an 8-person team to develop large-scale machine learning systems for monitoring and analyzing human health using wearable sensors, achieving a 30% increase in data accuracy and reliability.
● Developed algorithms to analyze human body status using fully customized Transformers and deep learning frameworks including NumPy, Pandas, PyTorch and TensorFlow, resulting in a 25% improvement in detection precision.
● Completed a brain injury detection algorithm by implementing a deep learning-based Gaze-Tracking algorithm and Transformer-based NLP model, which enhanced early detection rates by 40%.
● Developed a deep learning-based GPS-denied navigation system for proof of concept (POC) product development, achieving navigation accuracy within a 5-meter radius in 97% of tests.
● Managed big data processing by implementing data pipelines like ETL using AWS services and frameworks and libraries including NumPy, Pandas, Dask, PySpark, and Apache Spark, reducing data processing time by 35%.
● Created and maintained training, data handling, and monitoring jobs for machine learning models on Amazon SageMaker, integrating them with customer-facing web applications, leading to a 20% increase in user engagement.
● Developed an in-house machine learning training and serving portal leveraging DevOps and AWS experience, which sped up development and testing processes by 50%.
Senior Data Engineer at
Scale AI
2018
-
2021
● Created scalable Kubeflow/Kubernetes-based machine learning training and monitoring pipelines.
● Developed, deployed, and optimized ETL & ELT pipelines on Amazon Web Services using a combination of Amazon Redshift, Amazon SNS (Simple Notification Service), AWS Glue, Amazon S3, Amazon EMR, AWS Step Functions, and AWS Lambda, integrated with Apache Spark for data processing.
● Implemented secure, reliable data workflows on Google Cloud Platform (GCP), utilizing services like BigQuery and Cloud Dataflow for optimal data management.
● Designed and populated an Enterprise Data Warehouse and data marts, ensuring high-quality data for business intelligence applications.
● Designed and implemented CI/CD pipelines using GitHub Actions, enabling automated testing and deployment of applications to GCP services.
● Containerized applications using Docker, streamlining development and ensuring consistency across multiple environments.
● Managed Kubernetes clusters for orchestration and optimized scalability and reliability of microservices-based applications, leading the integration of GCP's Cloud Build and Artifact Registry within the workflows.
Python Engineer at
Nike
2015
-
2018
● Orchestrated complex data workflows, ensuring smooth execution and task dependencies, enabling efficient processing and analysis of large datasets using Apache Airflow, Apache Spark, Apache Kafka and Amazon Redshift.
● Built self-serve Tableau and Mode dashboards for the team to monitor daily metrics and key feature launch performance.
● Guided integration of various databases into a single data source, using practices and strategies that have been scaled out to other data platforms.
● Built a recommendation engine pipeline by exploring multiple machine learning algorithms to create user segments for better network traffic management and customization of users' preferences, and deployed using Django and Flask on the AWS platform.
● Architected the scalable backend systems and APIs for building the SAAS platform for ERP & CRM data analytics.
● Trained, Evaluated and tested several ML models for the fields in OCR, Audio Signal.
● Processing, and Time Series Analysis, improving the accuracy by 15% and deployed them with Django, Flask, FastAPI, Docker, Kubernetes.
● Dockerized, deployed, and built CI/CD pipelines for several AI products with Git, CircleCI, Jira.
● Utilized several ETL & ELT tools for scalable data-driven solutions and pipelines.
Freelancer at
Upwork
2014
-
2015
● Accurately wrote more than 100 Python scripts to automate the ETL script runs every hour.
● Utilized AWS to manage cloud hosting, ensuring reliable and scalable performance for the company’s web applications, leading to improved user experience and customer satisfaction.
● Used the Python Django framework to interface with the jQuery UI, managing the storage and deletion of 50+ contents.
Education
Bachelor of Computer Science at
Texas Tech University
2009
-
2013