AI/ML Engineer - LLMs, RAG & Multimodal Systems

Aasritha Bhimisetty

+1-201-***-**** ********************@*****.*** New Jersey

PROFESSIONAL SUMMARY:

AI/ML Engineer with 3+ years of experience building and deploying production-grade AI systems, including agent-based architectures, multimodal pipelines, and real-time decision platforms. Expertise in Python, PyTorch, TensorFlow, and Hugging Face, with hands-on experience in LLMs, NLP, computer vision, and streaming data from video and IoT sensors. Skilled in designing scalable end-to-end solutions integrating AI with enterprise systems, leveraging MLOps tools such as Docker, Kubernetes, MLflow, and Airflow. Proven ability to translate research into practical applications, delivering reliable, high-performance systems in fast-paced, data-intensive environments.

TECHNICAL SKILLS:

Languages: Python, SQL, JavaScript, TypeScript

Machine Learning & Deep Learning Libraries: Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, LightGBM, OpenCV NLP & Generative AI: spaCy, NLTK, Hugging Face Transformers, LangChain, FastText, BERT, GPT-4, T5, LLaMA, RAG, Prompt Engineering, Semantic Search

Computer Vision & Multimodal AI: YOLO, Image Segmentation, Object Detection, Action Recognition Agent Systems & AI Architecture: Agent-based Systems, AI Orchestration, Tool Integration, State Management, Human-in-the-loop Big Data & Streaming: Apache Spark, Kafka, Hadoop, Hive, Databricks Cloud Platforms: AWS (S3, EC2, SageMaker, Lambda, Glue, Bedrock), Azure (ML, Databricks), GCP (BigQuery, AI Platform) Databases: PostgreSQL, MySQL, MongoDB, Cassandra

MLOps / CI-CD: MLflow, Kubeflow, Airflow, Docker, Kubernetes, Jenkins, GitLab CI, Prometheus, Grafana Backend & Development: FastAPI, Flask, REST APIs, Microservices, Node.js Data Engineering & Processing: Data Ingestion, Feature Engineering, Time-Series Analysis, FFT, Data Normalization, Schema Mapping Visualization & Monitoring: Tableau, Power BI, Plotly, Dash, Matplotlib, Seaborn Machine Learning Techniques: Regression, Classification, Clustering, Time Series, Anomaly Detection, PCA Deep Learning Models: CNN, RNN, LSTM, Transformers, GANs, Autoencoders, ResNet PROFESSIONAL EXPERIENCE:

Client: Epic Systems, Houston, TX, USA. Employment Duration: May 2024 – Till Present Role: AI/ML Engineer / Gen AI/LLM Specialist Agentic AI Developer Responsibilities

• Designed and led the development of an AI-driven real-time insurance underwriting and adaptive pricing system, enabling personalized solutions and significantly enhancing risk prediction and operational efficiency.

• Built and deployed agent-based AI systems in production using orchestration frameworks, enabling tool usage, state management, and human-in-the-loop workflows for operational decision-making.

• Implemented multimodal AI pipelines combining computer vision models (YOLO, segmentation, action recognition) with structured operational and transactional data.

• Processed and analyzed large-scale real-world data streams including RTSP video feeds, time-series telemetry, vibration, and thermal sensor data while handling noise, drift, and latency challenges.

• Developed end-to-end AI solutions from data ingestion and preprocessing to model training, serving, and backend integration using Python and TypeScript.

• Engineered agent runtime systems supporting context assembly, tool dispatch, approval gates, tracing, and cost monitoring for scalable AI operations.

• Created and optimized feature extraction pipelines using FFT and cross-sensor correlation techniques for predictive insights and anomaly detection.

• Designed and maintained ontology and knowledge graph structures representing assets, sensors, workflows, and maintenance histories to enable contextual intelligence.

• Integrated AI platforms with enterprise systems such as ERP, CMMS, WMS, and PLC layers, ensuring seamless data normalization and schema mapping.

• Built memory layers including trace storage, reusable playbooks, and failure pattern libraries to enhance agent learning and decision consistency.

• Developed dashboards and alerting workflows with evidence-backed insights, enabling real-time monitoring, operational planning, and audit trails.

• Implemented backend services and APIs to support model inference, data pipelines, and frontend interfaces for operational visibility.

• Addressed complex challenges in data ingestion, temporal alignment, and ground truth validation for high-quality model performance.

• Continuously improved system robustness by analyzing failure modes, optimizing latency, and enhancing human-AI interaction workflows.

Client: ZenQ, Hyderabad, TG, India. Employment Duration: Mar 2022 – Jul 2023 Role: AI/ML Engineer Responsibilities:

• Led applied research initiatives leveraging generative AI and large language models to solve complex business problems and improve automation capabilities.

• Designed, developed, and deployed scalable machine learning and NLP systems in production environments using Python, PyTorch, and TensorFlow.

• Translated research concepts and academic publications into practical, production-ready AI solutions for real-world applications.

• Built and fine-tuned LLM-based models for tasks such as text generation, summarization, classification, and semantic search.

• Collaborated with cross-functional teams to prototype, test, and iterate on emerging NLP solutions in fast-paced environments.

• Implemented data preprocessing pipelines for large-scale text datasets, including cleaning, tokenization, and feature engineering.

• Optimized model performance through hyperparameter tuning, evaluation metrics, and continuous experimentation.

• Developed REST APIs and backend services to integrate NLP models into enterprise applications and workflows.

• Monitored and maintained deployed models, ensuring scalability, reliability, and performance in data-intensive systems.

• Conducted research on latest advancements in generative AI, staying updated with emerging trends, tools, and frameworks.

• Built end-to-end pipelines from data ingestion to model deployment, ensuring seamless integration and automation.

• Worked closely with stakeholders to understand requirements and deliver AI-driven solutions with business objectives.

• Improved model accuracy and efficiency by leveraging transfer learning, prompt engineering, and fine-tuning techniques. EDUCATION:

• Masters in Computer Science Pace University, 2025

• Bachelors in Computer Science and Engineering Usha Rama College of Engineering and Technology, 2023 CERTIFICATIONS :

• SnowPro Core Certification.

• Databricks Certified Generative AI Engineer Associate certification.

• AWS Certified Machine Learning – Specialty

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