Machine Learning Ml Engineer

Preethi Reddy

ML Engineer (Generative AI)

+ Marlborough, MA # ************@*****.*** +1-609-***-**** SUMMARY

Machine Learning Engineer with over 5 years of experience designing, developing, and deploying robust ML solutions in e-commerce, telecom, and healthcare domains. Skilled in building full-stack AI systems with expertise in recommendation engines, predictive analytics, RAG pipelines, and LLM applications. Adept at end-to-end delivery—data ingestion, model training, scalable deployment, and monitoring—across cloud-native environments using tools like PyTorch, LangChain, MLflow, HuggingFace, FastAPI, and Azure. Known for bridging technical solutions with business goals and cross-functional collaboration. EXPERIENCE

ML Engineer (Gen AI) Jan 2023 – Present

Staples Inc Framingham, MA

• Spearheaded the development of a GPT-4-powered internal assistant using LangChain and Azure OpenAI, enabling employees to query organizational knowledge in natural language.

• Designed a scalable RAG architecture using FAISS and Azure Cognitive Search, transforming fragmented product documentation into searchable knowledge graphs.

• Implemented prompt auditing with Guardrails AI and TruLens to ensure brand tone consistency, reduce hallucinations, and log user feedback for continuous improvement.

• Built containerized inference APIs using FastAPI and Docker, integrated into AKS with GitHub Actions and Azure Pipelines for automated deployment and versioning.

• Led document embedding and semantic search integration via Sentence Transformers and Pinecone, improving retrieval relevance for long-form PDF and FAQ queries.

• Built Streamlit-based observability dashboards and Grafana monitors to visualize prompt response rate, latency, and semantic coverage of retrieved results.

• Partnered with legal and governance teams to align chatbot behavior with corporate compliance and internal knowledge retention policies.

ML Engineer May 2021 – Dec 2022

Wipro (Client: AT&T) Remote

• Designed predictive ML pipelines using historical network logs and streaming metrics to proactively forecast peak congestion and reduce user-impacting packet loss.

• Developed anomaly detection systems with LSTM and Isolation Forest to trigger alerts for infrastructure outages and equipment failure, improving resolution speed.

• Led development of churn prediction models using call center logs, service plan usage, and user behavior for targeted marketing and retention strategy.

• Integrated Kafka and Apache Spark with Airflow DAGs to orchestrate real-time ML model updates and scheduled batch scoring workflows.

• Deployed monitoring stack using Prometheus and Grafana to track model inference drift, latency spikes, and API uptime in production.

• Built text processing pipelines using spaCy and BERT to extract root cause insights from thousands of customer complaint tickets and transcripts.

• Collaborated with cross-functional teams to map ML outcomes to operational KPIs, ensuring alignment with AT&T’s business and support objectives.

Data Scientist Jun 2020 – Apr 2021

Accenture Hyderabad, India

• Engineered healthcare risk score models using logistic regression and XGBoost, analyzing claims, pharmacy, and clinical procedure data for cost forecasting.

• Merged disparate data sources including ICD-10/CPT codes, diagnosis history, and demographics to build enriched feature views with longitudinal coverage.

• Built SQL and Python ETL pipelines to clean and standardize payer datasets for consumption by analytics dashboards and ML pipelines.

• Designed and presented model insights to actuarial analysts and healthcare delivery managers, supporting strategy for high-risk outreach.

• Implemented MLflow pipelines to track model versions, training parameters, and output metrics for reproducible healthcare model governance.

• Conducted fairness audits and explainability reports using SHAP and LIME to assess bias in prediction output across protected attributes.

• Helped integrate predictive models into care team platforms, improving early detection workflows and triaging logic.

EDUCATION

Bachelor of Technology in Computer Science and Engineering 2017 – 2021 TKR college of Engineering & Technology, Hyderabad SKILLS

Languages & ML Frameworks: Python, TensorFlow, PyTorch, scikit-learn, XGBoost, LightGBM, CatBoost, NumPy, Pandas, Keras

ML & Modeling: Logistic Regression, Random Forest, LSTM, ARIMA, GPT-based models, Clustering, Causal Inference, Forecasting, SVM, PCA, Time Series Analysis Model Ops & Monitoring: MLflow, SHAP, LIME, Weights & Biases, Prometheus, Grafana, Neptune.ai, Sentry, Model Drift Detection

NLP & AI Tooling: OpenAI API, Azure OpenAI, HuggingFace, LangChain, SpaCy, NLTK, OCR, Transformers, TextBlob, Gensim

Data Engineering: Apache Spark, Kafka, Airflow, dbt, Snowflake, BigQuery, Azure Data Factory, SQL, Delta Lake, Parquet

Deployment & APIs: FastAPI, Flask, Docker, Kubernetes, AWS (SageMaker, EC2, S3, Lambda), Azure ML, GCP AI, REST, gRPC

Automation & Testing: Selenium, Playwright, Jenkins, GitHub Actions, CI/CD, Git, Unit Testing, Load Testing Visualization: Tableau, Power BI, Dash, Matplotlib, Seaborn, Plotly Collaboration: Agile, JIRA, Zephyr, Confluence, Stakeholder Reporting, Slack, Notion, Google Workspace PROJECTS

• Sentiment Analysis: Implemented a classification pipeline using logistic regression and SVM to analyze retail customer reviews; delivered sentiment trends to CX team with supporting visual dashboards.

• CNN for Image Classification: Developed a convolutional neural network using TensorFlow with dropout, batch normalization, and data augmentation to reach 80

• Network Anomaly Detection: Applied Isolation Forest and DBSCAN to streaming telecom logs for real-time anomaly detection; improved alerting precision and reduced false positives.

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