AI/ML Engineer

Navya Reddy

AI/ML Engineer **************@*****.*** 602-***-****

Location: Phoenix, AZ-85032

PROFESSIONAL SUMMARY:

AI/ML Engineer with 7+ years of experience building scalable production systems, specializing in deep learning, NLP, LLM integration, and end to end ML lifecycle deployment across cloud environments.

Proven expertise in AWS deployments using EC2, S3, RDS, SageMaker, and IAM, supported by Docker, CI/CD pipelines, MLflow, and Linux environments.

Experienced in Scikit-learn, PyTorch, TensorFlow, Hugging Face, Flask, FastAPI, and Django to engineer production-grade platforms.

Well-rounded in managing the complete ML lifecycle, including EDA, preprocessing, embeddings, vector search, model monitoring, drift detection, and retraining automation.

Technically adept with relational and NoSQL databases, ETL pipelines, Celery-based task queues, Redis caching, and asynchronous processing frameworks.

Specialized in AI and machine learning, delivering measurable business impact through predictive modeling, NLP systems, LLM pipelines, and feature engineering.

Demonstrated adaptability and problem-solving mindset, consistently delivering high-quality solutions in fast-paced, Agile environments.

Quality-driven with experience implementing OAuth 2.0, RBAC, observability tooling, automated testing, logging, monitoring, and production support.

Effectively communicated technical insights and ML results to non-technical stakeholders, enabling informed business decisions and collaborative solutions.

TECHNICAL SKILLS:

Full Stack Engineering & APIs: Python (3.x), RESTful API Design, Microservices Architecture, SDLC, HTML5, CSS3, JavaScript, Bootstrap, jQuery

AI, Machine Learning & Data Science: Machine Learning Development, Predictive Analytics, Feature Engineering, Model Training & Optimization, Model Evaluation (Accuracy, F1-Score, RMSE), Deep Learning, NLP, LLMs, Prompt Engineering, EDA, Data Cleaning, Preprocessing, Normalization, Embeddings, Vector Search, Drift Detection, Model Monitoring, Automated Retraining, End-to-End ML Lifecycle

Frameworks & Application Platforms: Scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, FAISS, Flask, FastAPI, Django, Django REST Framework

Data Platforms & Pipelines: Data Pipelines, ETL, MySQL, PostgreSQL, MongoDB

Cloud Engineering & MLOps: AWS (EC2, S3, RDS, SageMaker, IAM), Linux, Docker, MLflow, Jenkins, CI/CD Pipelines, Experiment Tracking, Model Versioning, Joblib, Pickle

Distributed Systems & Messaging: Celery, Redis

Visualization & UI Analytics: Matplotlib, Plotly

Security, Observability & Testing: OAuth 2.0, RBAC, Logging, Error Handling, Metrics Monitoring, Production Debugging, Pytest, Unit Testing, Integration Testing

Delivery & Engineering Practices: Git, Code Reviews, Agile, Scrum, Jira, Confluence, Technical Documentation

EXPERIENCE:

Goldwater Bank, Scottsdale, AZ Mar 2025 Present

Role: AI/ML Engineer

Responsibilities:

Released containerized applications with Docker while maintaining Git based version control and peer reviews.

Built deep learning pipelines with PyTorch for pattern discovery and large scale data processing.

Developed AI powered SMS and in app messaging services enabling customers to perform bill payments, balance inquiries, and account updates, reducing call center dependency and improving digital engagement.

Built conversational NLP pipelines to interpret customer intents and automate routine banking transactions through mobile and SMS channels.

Integrated vector database indexing with LLM pipelines to support Retrieval Augmented Generation (RAG), improving contextual response relevance and reducing hallucination in customer interactions.

Implemented MLflow for experiment tracking, version control, and cross environment governance.

Launched cloud workloads on AWS EC2, S3, and SageMaker to support scalable model execution.

Developed MySQL backed data pipelines and transformation workflows to support training and inference processes.

Delivered Python based full stack applications integrating machine learning for predictive analytics and workflow automation.

Operationalized monitoring, drift detection, and automated retraining to reduce model degradation incidents by 30% in production.

Tuned Scikit- learn models to increase prediction accuracy by 18% for production pipelines.

Established reusable ML pipelines spanning experimentation, validation, and production rollout.

Strengthened reliability through centralized logging, fault handling, and runtime observability.

Integrated LLM driven response generation to enhance real time customer interactions while maintaining compliance and security standards.

Integrated LLM powered services through prompt design and Hugging Face APIs for enterprise text generation use cases.

Published models through FastAPI driven REST services for real time and batch inference.

Refined datasets through feature engineering, preprocessing, and normalization to improve consistency and reliability.

Deployed semantic search solutions using FAISS and vector embeddings to accelerate retrieval and relevance.

Utilized Hugging Face Transformers for summarization, classification, and embedding workflows.

Standardized deployment workflows using Joblib based model packaging and rollback controls.

Collaborated within Agile teams through sprint planning, documentation, and cross functional delivery.

Environment: Python, Docker, AWS EC2, S3, SageMaker, PyTorch, FastAPI, REST APIs, MySQL, Scikit-learn, Hugging Face Transformers, FAISS, Joblib, MLflow, Git, Agile/Scrum

InovarTech, India May 2022 - Feb 2025

Role:Python Developer - Data Science

Responsibilities

Refined datasets through Pandas and NumPy based cleansing, normalization, and feature creation, improving model readiness by 20%.

Recorded experiments, modeling decisions, and workflows in Jupyter notebooks to improve reproducibility and knowledge transfer.

Implemented a Retrieval Augmented Generation (RAG) pipeline using vector embeddings and a vector database to enable semantic retrieval of analytical datasets, improving insight discovery and model traceability.

Released containerized solutions through Docker across AWS EC2, S3, and SageMaker environments.

Structured scalable schemas with Django ORM, PostgreSQL, and MongoDB to sustain growing analytical workloads and data pipelines.

Conducted exploratory analysis to surface anomalies, behavioral trends, and predictive signals for algorithm selection.

Governed repositories using Git workflows, pull request reviews, and controlled branching strategies.

Trained regression, classification, and clustering models with Scikit-learn to support forecasting and segmentation use cases.

Sustained production systems by applying monitoring pipelines, drift detection methods, and retraining workflows.

Developed interactive dashboards and reports with Matplotlib and Plotly, reducing manual reporting by 35%.

Executed full cycle analytics workflows covering ETL ingestion, processing, and transformation for diverse datasets.

Packaged trained models with Pickle to support repeatable deployments and cross environment portability.

Delivered data driven platforms using Python, Django REST Framework, FastAPI, and REST APIs to support analytics and real time model delivery.

Validated model outcomes through Accuracy, F1 score, and RMSE metrics to meet technical and operational expectations.

Implemented TensorFlow driven deep learning solutions for predictive modeling across large scale datasets.

Contributed within Agile delivery cycles through sprint planning, backlog grooming, and incremental releases.

Environment: Python, Pandas, Django, NumPy, Scikit-learn, TensorFlow, Docker, AWS, PostgreSQL, MongoDB, FastAPI, DRF, REST APIs, Matplotlib, Plotly, Pickle, Git, Agile/Scrum, Jupyter Notebook

OpenXcell, India Feb 2020 Apr 2022

Role: Software Engineer

Responsibilities:

Automated build and release pipelines through Jenkins to streamline deployments and reduce operational overhead.

Structured data persistence layers using MySQL, MongoDB, and SQLAlchemy to support efficient modeling and query performance.

Supported Linux hosted production systems by resolving issues, tuning performance, and maintaining runtime stability.

Packaged services into Docker containers to maintain consistent development and deployment workflows across environments.

Strengthened service security by integrating OAuth 2.0 based authentication and role based authorization controls.

Delivered scalable backend services with Python 3.x and Flask while defining REST APIs aligned to microservices based systems.

Operated within Agile delivery models using Jira for sprint tracking and Confluence for technical documentation.

Established monitoring and logging frameworks with metrics collection and proactive debugging for production reliability.

Provisioned and maintained AWS infrastructure across EC2, S3, RDS, and IAM to support secure and elastic cloud platforms.

Deployed and managed Kubernetes Jobs and CronJobs to execute scheduled batch processes and background workloads reliably across environments.

Built CI/CD pipelines integrating Docker image builds with Kubernetes deployments using Git based workflows.

Configured centralized logging and monitoring for Kubernetes workloads using Prometheus and Grafana.

Orchestrated asynchronous workloads with Celery and Redis to accelerate background processing and system responsiveness.

Validated system stability through Pytest driven unit and integration test suites.

Environment: Python 3.x, Docker, AWS EC2, S3, RDS, IAM, Kubernetes, Flask, REST APIs, Microservices, MySQL, MongoDB, SQLAlchemy, Celery, Redis, Jenkins, OAuth 2.0, Pytest, Linux, Agile/Scrum, Jira, Confluence

Ozrit, India Oct 2018 - Jan 2020

Role: Software Developer

Responsibilities:

Governed repositories through Git workflows, branching strategies, and peer code reviews to enforce development standards.

Built responsive front end components using HTML5, CSS3, JavaScript, Bootstrap, and jQuery to ensure cross-browser compatibility and mobile readiness.

Conducted unit testing, debugging, and issue resolution to preserve release quality and long term system reliability.

Delivered Django based web platforms from requirements through production while supporting high traffic user activity and maintaining strong system availability.

Released and supported applications using Linux servers and Apache to maintain stable production operations.

Implemented REST APIs with role based access control and session handling to strengthen security and limit unauthorized usage.

Structured PostgreSQL databases and optimized SQL queries to improve performance, scalability, and data consistency.

Environment: Python, HTML5, CSS3, Django, REST APIs, Bootstrap, PostgreSQL, JavaScript, Linux, Apache, Git, jQuery, Agile/Scrum, SDLC, MVT

EDUCATION:

Bachelor s Degree, Electronics & Communication Engineering (ECE), Sreenidhi Institute of Science and Technology (SNIST), India.

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