Senior Python GenAI/ML Engineer

Prakash L

SR. PYTHON DEVELOPER AI/ML ENGINEER

+1-972-***-**** *******.***********@*****.***

PROFILE SUMMARY

Senior Python Developer and GenAI Engineer with 5+ years of hands-on experience building production-grade AI/ML systems across healthcare, industrial engineering, and large-scale e-commerce platforms. Strong background in Python-driven microservices, LLM-based applications, and cloud-native architectures on AWS and GCP.

•Proven experience delivering end-to-end AI solutions including RAG pipelines, vector search, predictive models, and real-time data processing systems using PyTorch, TensorFlow, LangChain, and cloud MLOps practices.

•Expert in containerized deployments using Docker and Kubernetes, CI/CD automation with GitLab and ArgoCD, and designing secure, scalable backend APIs with FastAPI and Flask.

SKILLS

Languages

Python, SQL, JavaScript, TypeScript

Frameworks & Libraries

Django, Flask, FastAPI, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, XGBoost, Hugging Face Transformers

Cloud Platforms

AWS (S3, EC2, Lambda, SageMaker, Bedrock), Azure (Functions, Blob, Data Lake), GCP (Vertex AI, Pub/Sub)

DevOps & CI/CD

Docker, Kubernetes, Terraform, Jenkins, GitHub Actions, GitLab CI, ArgoCD

Databases

PostgreSQL, DynamoDB, MongoDB, SQL Server, Oracle

AI/ML

NLP, CNN, RNN, LSTM, RAG Pipelines, MLOps, Feature Engineering, Model Deployment, LangChain

Frontend

React.js, Angular, Vue.js, HTML5, CSS3, Bootstrap

Visualization

Tableau, Power BI, Seaborn, Matplotlib

Collaboration Tools

Git, Jira, Confluence, Postman, Jupyter Notebook, VS Code, PyCharm

Methodologies

Agile (Scrum), TDD, CI/CD, Data Wrangling, ETL, Microservices

WORK EXPERIENCE

Client: Citi, Irving, Texas Mar 2023 – Present

Role: Sr. Python Developer with AI/ML

•Designed and built scalable Python microservices using FastAPI to support HIPAA-compliant patient care coordination and analytics workflows.

•Developed GenAI-based RAG pipelines for summarizing large clinical documents using LangChain, vector databases, and LLMs, significantly reducing physician review time.

•Implemented PyTorch and TensorFlow models for patient readmission prediction and risk scoring integrated into discharge planning systems.

•Built secure LLM-powered internal chat assistants for clinicians using MCP-based access control and audit logging.

•Designed vector search systems for clinical research documents using embedding pipelines and semantic similarity ranking.

•Automated AI workflows using n8n, orchestrating data ingestion, model inference, and downstream notifications.

•Integrated AWS SageMaker pipelines for model training, versioning, and automated production deployments.

•Deployed containerized AI services using Docker and Kubernetes (EKS) with horizontal auto-scaling.

•Developed real-time Kafka-based event streams to propagate patient status updates and clinical alerts.

•Built ETL pipelines using Python, Pandas, and PySpark to process EHR and claims data into analytics-ready formats.

•Implemented DynamoDB and PostgreSQL for low-latency and transactional healthcare data access.

•Designed secure REST APIs with OAuth2 and JWT for AI inference endpoints.

•Integrated AWS Bedrock and managed LLM access layers for protected healthcare use cases.

•Applied feature engineering pipelines to standardize model inputs across multiple data sources.

•Developed explainability modules for ML predictions to support clinical audits and compliance review.

•Enabled population health cohort analysis by combining predictive modeling with real-time data feeds.

•Implemented data encryption and key management using AWS KMS for PHI protection.

•Automated CI/CD pipelines using GitLab and ArgoCD, enabling safe and repeatable deployments.

•Built API testing frameworks using PyTest, achieving high reliability across AI services.

•Developed React-based dashboards to visualize patient risk scores and ML-driven insights.

•Developed VectorDB-based semantic search for internal medical research documents, reducing time to insights for clinicians.

•Mentored junior engineers on Python best practices, model interpretability techniques, and cloud deployment strategies.

Environment/Tools: FastAPI, Python, LangChain, PyTorch, TensorFlow, AWS (SageMaker, Bedrock, EKS, DynamoDB, KMS, S3, Lambda), Docker, Kubernetes, Kafka, PySpark, PostgreSQL, GitLab CI, ArgoCD, PyTest, React.js, n8n, Vector Databases

Client: Verizon, Irving, TX Jan 2022 – Feb 2023

Role: Python Developer with AI/ML

•Built Python-based backend services using FastAPI and Flask for additive manufacturing platforms.

•Developed PyTorch-based predictive maintenance models analyzing sensor telemetry from industrial printers.

•Implemented real-time data ingestion pipelines using Kafka and GCP Pub/Sub for continuous monitoring.

•Designed ML inference services deployed via Docker and Kubernetes for low-latency predictions.

•Integrated AWS and GCP cloud services for scalable data processing and storage.

•Created vector-based similarity engines to compare 3D print patterns and detect defects early.

•Built asynchronous task processing using Celery for long-running optimization jobs.

•Developed Angular dashboards to visualize printer health, error rates, and ML insights.

•Applied TensorFlow models to analyze layer-by-layer print quality.

•Implemented secure authentication using OAuth2 and JWT for internal APIs.

•Used PostgreSQL and S3-based data lakes to store historical manufacturing data.

•Automated CI/CD pipelines using GitLab CI, improving deployment consistency.

•Integrated ArgoCD to manage Kubernetes deployments across environments.

•Developed ML-based topology optimization tools for additive parts, reducing material costs and improving mechanical properties.

•Built internal quality assurance tools using Python and TensorFlow to evaluate layer-by-layer print consistency.

•Optimized data preprocessing steps using NumPy and Dask, achieving significant reductions in model training times.

•Led knowledge-sharing sessions on AI integration with industrial automation, improving team capabilities across global teams.

Environment/Tools: FastAPI, Flask, PyTorch, TensorFlow, Kafka, GCP Pub/Sub, Docker, Kubernetes, AWS (S3), GCP, PostgreSQL, Angular, Celery, GitLab CI, ArgoCD, NumPy, Dask, OAuth2, JWT

Client: Macy’s

Company: NESS Technologies, Hyderabad, India May 2020 – June 2021

Role: Jr Python Engineer

•Developed Python microservices for order processing and inventory management.

•Built AI-based recommendation engines using collaborative filtering and similarity models.

•Implemented LLM-assisted search enhancements for product discovery.

•Integrated vector embeddings to improve semantic product matching.

•Developed Flask APIs for promotions and personalized pricing logic.

•Built React and Vue components for real-time product personalization.

•Designed SQL and NoSQL schemas for high-traffic transactional systems.

•Implemented fraud detection models using anomaly scoring techniques.

•Deployed Dockerized services on Kubernetes for peak sale events.

•Optimized checkout latency through caching and query tuning.

•Integrated third-party logistics APIs for live shipment updates.

•Built analytics dashboards to track sales and conversion metrics.

•Optimized database queries and caching strategies for high-traffic periods, reducing checkout timeouts and abandonment rates.

•Collaborated with design and product teams to implement accessibility features, achieving WCAG 2.1 compliance.

•Documented API specifications with Swagger and led code reviews to enforce best practices and maintain code quality.

Environment/Tools: Python, Flask, Docker, Kubernetes, React.js, Vue.js, SQL, MongoDB, Redis, Swagger, Scikit-learn, NumPy, Pandas, Git, Jira

PUBLICATIONS

L. Oam Prakash, "Prediction and Comparative Analysis of Air Pollution in Major cities of India using Deep Learning Techniques," 2021 Second International Conference on Electronics and Sustainable Communication Systems (ICESC), Vijayawada, India, 2021, pp. 1434-1439, doi: 10.1109/ICESC51422.2021.9532860.

COURSES & CERTIFICATIONS

•Hands-on Python and R in Data Science – Udemy

•Machine Learning: Accurate Predictions and Model Selection – Udemy

•Cybersecurity: Attacks on Systems, DoS/DDoS, SQL Injection, Session Hijacking, and Wireless Security

EDUCATION

Master of Science in Computer Science – LAMAR University, Beaumont, Texas.

Bachelor of Science in Computer Science – K L university, Vijayawada, India.

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