AI/ML Engineer - LLMs, NLP, MLOps, Cloud-First Solutions

Diwita Banerjee

AI/ML Engineer

+1-703-***-**** ****************@*****.*** LinkedIn GitHub Open to Relocate PROFESSIONAL SUMMARY

AI/ML Engineer with 5+ years of experience designing and deploying production-grade machine learning solutions in financial services, healthcare, and enterprise domains. Skilled in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), and MLOps. Experienced in fine-tuning Transformer models, building scalable APIs, and deploying AI systems on AWS, GCP, and Azure. Delivered LLM-powered pipelines that improved document retrieval accuracy by 30% and reduced inference latency by 40%. Automated ML workflows with Airflow, MLflow, Jenkins, and Docker, cutting deployment cycles by 50%. Strong background in regulatory-compliant AI, feature engineering, and real-time analytics. Adept at collaborating with cross-functional teams to drive business impact through AI adoption. TECHNICAL SKILLS

● Languages & IDEs: Python, SQL, Shell Scripting, MATLAB, Jupyter Notebook, VS Code, Google Colab, SSMS

● Frameworks & Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch, XGBoost, Matplotlib, Seaborn, NLTK, spaCy, Sentence-BERT, Hugging Face Transformers, LangChain

● Machine Learning: Logistic Regression, Linear Regression, Decision Trees, Random Forests, SVM, Naive Bayes, A/B Testing, Model Evaluation, Feature Engineering

● Deep Learning & NLP/LLMs: CNN, RNN, LSTM, BERT, RoBERTa, GPT-3/4, LLMs, Prompt Engineering, RAG pipelines, Fine-tuning (LoRA/PEFT)

● MLOps & Deployment: Docker, Kubernetes (EKS), FastAPI, Flask, MLflow, Jenkins, Terraform, Airflow, PySpark, Model Drift Detection, Usage Metrics Dashboards

● Cloud & Visualisation: AWS (Lambda, EC2, S3, RDS, SageMaker, SQS, SNS, CodeDeploy, CloudWatch, API Gateway), GCP, Azure, Tableau, Power BI

● Databases & Tools: PostgreSQL, MySQL, MongoDB, Cassandra, Redis, Neo4j, SQL Server, SQLAlchemy

● Statistical Techniques: Hypothesis Testing, Data Modeling, Data Visualization, Experiment Design, A/B Testing

● Collaboration Tools: Agile Development, Requirements Gathering, Stakeholder Engagement, AI Insights Visualization PROFESSIONAL EXPERIENCE

Freddie Mac AI/ML Engineer Dec 2024 – Present

● Designed and implemented LLM-powered RAG pipelines using FAISS and Hugging Face, increasing financial document retrieval accuracy by 30%.

● Built and deployed FastAPI-based microservices on AWS EKS and Lambda, reducing model inference response time by 40% and improving scalability.

● Fine-tuned Transformer models, including BERT and RoBERTa, on compliance-related datasets, improving contextual relevance by 25%.

● Automated end-to-end ML lifecycle management using Airflow, MLflow, Jenkins, and Docker, shortening deployment cycles by 50%.

● Developed monitoring dashboards with PySpark and AWS CloudWatch for drift detection and performance analysis of deployed models.

● Collaborated with quantitative analysts and compliance officers to deliver regulator-compliant AI solutions with transparent auditability.

George Mason University AI Research Assistant May 2024 – Present

● Built fairness and bias evaluation pipelines for multimodal AI models, including Stable Diffusion, LLaVA, and InstructBLIP, uncovering demographic disparities.

● Developed a medical VQA system using MIMIC-IV EHR data that reduced clinician query resolution time by 25% in controlled trials.

● Optimized large-scale training and inference workflows on the Hopper HPC cluster, reducing runtime by 40%.

● Designing and implementing RCRS pipelines with prompt injection defenses to strengthen the reliability and safety of generative AI systems.

Cognizant Technology Solutions Machine Learning Developer, Salesforce CRM Nov 2021 – Jul 2023

● Built predictive lead/case/renewal scoring models with Einstein Prediction Builder and automated nightly training and scoring via Apex batch and queueable jobs.

● Delivered prescriptive recommendations using Einstein Discovery and Next Best Action, boosting SLA compliance and agent productivity.

● Develop CRM Analytics datasets and dashboards to monitor win-rate lift, adoption, precision/recall, and model drift, with alerts for threshold deviations.

● Integrated external ML endpoints through MuleSoft and Named Credentials with retry, timeout, and audit logging, ensuring secure and reliable data exchange.

● Implemented event-driven and batch pipelines with Platform Events and REST APIs for near real-time updates and high-volume processing.

● Automated CI/CD pipelines with Git and Copado, added unit/integration tests for high coverage, and authored runbooks to streamline monitoring and rollback.

● Recognised with the “Working as One” (2023) and “Always Striving & Never Settling” (2022) awards. Larsen & Toubro Infotech AI Developer – Full Stack Applications Jun 2020 – Oct 2021

● Engineered AI-driven predictive analytics modules using Spring Boot, Python, and Oracle SQL, reducing backend response latency by 39%.

● Developed intelligent data-driven UI components with React.js, improving usability and long-term scalability.

● Automated defect root cause analysis workflows, reducing issue resolution time by 30%.

● Designed APIs for scalable integration of AI models into enterprise applications.

● Collaborated with business stakeholders to align AI feature development with operational requirements.

● Integrated backend analytics with enterprise dashboards for real-time monitoring and decision-making. RESEARCH & PROJECTS

MedBLIP – Medical Image Captioning (GMU, Spring 2025)

● Fine-tuned BLIP on the ROCO dataset for medical imaging captioning tasks.

● Achieved benchmark improvements across CIDEr, SPICE, and BERTScore.

● Compared performance with BLIP-2, InstructBLIP, and Gemini models. CloudMart—Multicloud AI E-Commerce Platform (Bootcamp, Mar 2025)

● Designed and deployed a GPT-powered shopping assistant across AWS, GCP, and Azure using Terraform, Kubernetes, and Docker.

● Implemented real-time product discovery, intelligent Q&A, and analytics pipelines.

● Built cross-cloud ETL-style AI pipelines with Git-based CI/CD, improving scalability and observability. News Summarisation & Simplification (GMU NLP Project, Fall 2024)

● Built an LSTM-based summarizer and T5 simplification model using Hugging Face Transformers and TensorFlow.

● Trained on the WikiAuto dataset and achieved BLEU 0.22 and Flesch-Kincaid readability 8.31.

● Delivered an end-to-end NLP pipeline to improve accessibility and comprehension of complex news content. Movie Recommendation (IJARESM, 2022)

● Developed a BERT-based semantic search and recommendation engine on the CMU Movie Summaries dataset.

● Enhanced semantic retrieval and recommendations with a 15% improvement in accuracy.

● Published results in the IJARESM research journal. EDUCATION

Master of Science in Computer Science (Machine Learning Concentration)Aug 2023 – May 2025 George Mason University, Fairfax, VA

● Coursework: Algorithms, AI, Data Mining, Machine Learning, Deep Learning, Advanced NLP

● Honours & Activities: Led and Won 2nd Position in GDG-GMU Hackathon; research on RCRS for Prompt Injection Mitigation

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