AI/ML Engineer - LLM & MLOps Specialist

Name: Srilakshmi Dasari

Email: ********************@*****.***

Ph.no: 330-***-****

Professional Summary:

• AI/ML Engineer with Around 4 years of experience designing, developing, and deploying scalable Machine Learning, Deep Learning, NLP, and Generative AI solutions across enterprise and academic research environments.

• Strong expertise in building end-to-end AI pipelines including data ingestion, feature engineering, model training, evaluation, deployment, monitoring, and continuous optimization.

• Hands-on experience developing Large Language Model (LLM) applications using OpenAI, Llama, Mistral, Hugging Face Transformers, Lang Chain, Retrieval-Augmented Generation

(RAG), and vector databases.

• Proficient in Machine Learning algorithms including Regression, Classification, Clustering, Ensemble Learning, Recommendation Systems, Time Series Forecasting, and Anomaly Detection.

• Extensive experience with Deep Learning frameworks such as TensorFlow, PyTorch, Keras, and Hugging Face for implementing CNNs, RNNs, LSTMs, Transformers, and Generative AI models.

• Skilled in MLOps practices including CI/CD automation, model versioning, experiment tracking, containerization, orchestration, model monitoring, and production deployment using MLflow, Docker, Kubernetes, and Airflow.

• Strong expertise in Natural Language Processing techniques including text classification, sentiment analysis, named entity recognition, document summarization, semantic search, question answering, and conversational AI systems.

• Experience working with cloud platforms including AWS, Azure, and GCP for scalable AI infrastructure, distributed training, serverless deployments, and managed machine learning services.

• Proficient in data engineering technologies including Python, SQL, Spark, Databricks, Pandas, NumPy, and ETL pipelines for processing large-scale structured and unstructured datasets.

• Skilled in implementing Responsible AI practices, model explainability, bias detection, performance optimization, and governance frameworks for enterprise AI applications.

• Adept at collaborating with cross-functional teams, stakeholders, researchers, and business units to translate complex business requirements into production-grade AI solutions.

• Strong understanding of software engineering principles, microservices architecture, API development, Agile methodologies, and DevOps practices supporting enterprise AI ecosystems.

Technical Skills:

Programming Languages: Python, R, SQL, Scala, Java, JavaScript, Shell Scripting Machine Learning: Supervised Learning, Unsupervised Learning, Reinforcement Learning, Ensemble Models, Recommendation Systems, Time Series Forecasting, Anomaly Detection, Predictive Analytics

Deep Learning: TensorFlow, PyTorch, Keras, CNN, RNN, LSTM, GRU, Autoencoders, GANs, Transformers

Generative AI & LLMs: OpenAI GPT, Llama, Mistral, Claude, Hugging Face Transformers, Lang Chain, Lang Graph, RAG, Prompt Engineering, Fine-Tuning, Embeddings, Semantic Search, Agentic AI

Natural Language Processing: NLTK, SpaCy, BERT, RoBERTa, Sentence Transformers, Text Classification, NER, Topic Modeling, Summarization, Question Answering MLOps & Deployment: MLflow, Kubeflow, Docker, Kubernetes, Airflow, Jenkins, GitHub Actions, CI/CD Pipelines, Model Monitoring, Model Registry

Cloud Platforms: AWS SageMaker, AWS Lambda, EC2, S3, Azure ML, Azure OpenAI, Databricks, GCP Vertex AI, Cloud Functions

Data Engineering: Apache Spark, PySpark, Databricks, Kafka, Hadoop, ETL Pipelines, Data Warehousing, Data Lake Architecture

Databases: PostgreSQL, MySQL, SQL Server, MongoDB, Cassandra, Elasticsearch, Pinecone, Chroma DB, FAISS, Weaviate

Visualization & BI: Power BI, Tableau, Matplotlib, Seaborn, Plotly, Streamlit Version Control & Collaboration: Git, GitHub, GitLab, Bitbucket, Jira, Confluence, Agile, Scrum Education:

• Masters in computer science, Kent State University, Kent, Ohio.

• Bachelors in Electronics and communication engineering, Anna University (DSEC), India.

Professional Experience:

Client: Yale University, New Haven, CT March 2024 – Present Role: AI/ML Engineer

Responsibilities:

• Designing and developing advanced AI and Generative AI solutions supporting academic research initiatives involving large-scale data analysis, knowledge discovery, and intelligent decision support systems.

• Built Retrieval-Augmented Generation (RAG) architectures integrating Large Language Models with vector databases to enable accurate and context-aware question answering across domain-specific knowledge repositories.

• Developed intelligent conversational AI systems utilizing OpenAI GPT, Llama, Hugging Face Transformers, Lang Chain, and prompt engineering techniques to enhance research productivity and information accessibility.

• Implemented advanced NLP pipelines for semantic search, document summarization, topic extraction, information retrieval, and automated literature review processes.

• Fine-tuned transformer-based language models using domain-specific datasets to improve contextual understanding, response quality, and task-specific performance metrics.

• Developed scalable vector search solutions using Pinecone, FAISS, and Chroma DB to support embedding-based retrieval and semantic similarity applications.

• Built AI agents and workflow automation systems capable of orchestrating multiple tools, APIs, and data sources to streamline research operations and analytical tasks.

• Leveraged deep learning architectures including BERT, RoBERTa, Llama, and custom transformer models for classification, extraction, ranking, and generative AI use cases.

• Designed MLOps frameworks supporting experiment tracking, model versioning, automated deployment, monitoring, and lifecycle management of machine learning assets.

• Implemented cloud-native AI solutions utilizing Azure OpenAI, AWS SageMaker, and GCP Vertex AI for scalable training, inference, and model serving capabilities.

• Conducted extensive model evaluation using quantitative and qualitative metrics while implementing explainability and bias assessment techniques for responsible AI adoption.

• Collaborated with researchers, professors, data scientists, and technical teams to identify research opportunities and deliver innovative AI solutions aligned with institutional objectives.

• Developed real-time APIs and microservices enabling seamless integration of AI capabilities into research platforms, web applications, and enterprise systems.

• Optimized model performance through distributed training, inference acceleration, prompt optimization, caching strategies, and efficient resource utilization techniques.

• Contributed to AI research initiatives involving LLM evaluation, knowledge graphs, multimodal learning, agentic AI systems, and emerging Generative AI technologies. Environment: Python, SQL, PyTorch, TensorFlow, Hugging Face, OpenAI API, Azure OpenAI, Lang Chain, Lang Graph, Llama, Mistral, RAG, Pinecone, Chroma DB, FAISS, BERT, RoBERTa, NLP, MLflow, Docker, Kubernetes, Airflow, Databricks, Apache Spark, AWS SageMaker, Azure ML, GCP Vertex AI, Fast API, GitHub Actions, REST APIs, PostgreSQL, MongoDB, Linux, Agile, Jira Client: MEL Systems and Services Ltd., India Sept 2022 – Dec 2023 Role: Software Engineer

Responsibilities:

• Designed and developed enterprise machine learning solutions to improve operational efficiency, automate business processes, and generate actionable insights from large-scale structured and unstructured datasets.

• Built end-to-end machine learning pipelines encompassing data collection, preprocessing, feature engineering, model training, validation, deployment, and performance monitoring across multiple business domains.

• Implemented supervised and unsupervised learning algorithms including Random Forest, XGBoost, Logistic Regression, K-Means Clustering, and Gradient Boosting models to address predictive analytics requirements.

• Developed Natural Language Processing solutions for document classification, sentiment analysis, entity extraction, and automated information retrieval from high-volume textual datasets.

• Engineered scalable ETL pipelines using Python, SQL, and Spark to process and transform large datasets from multiple enterprise systems while ensuring data quality and consistency.

• Created predictive maintenance models leveraging sensor and operational data that enabled proactive issue identification and reduced unexpected equipment downtime.

• Built anomaly detection frameworks utilizing statistical and machine learning techniques to identify unusual patterns, system failures, and operational deviations in real-time environments.

• Collaborated with business stakeholders and domain experts to understand operational challenges, define measurable success metrics, and translate requirements into AI-driven solutions.

• Optimized machine learning model performance through hyperparameter tuning, feature selection, cross-validation strategies, and ensemble learning approaches.

• Developed interactive dashboards and analytical reports using Power BI and Tableau to communicate model insights, KPIs, and business recommendations to leadership teams.

• Automated model deployment workflows through containerization and CI/CD pipelines, reducing deployment timelines and improving reproducibility across environments.

• Integrated machine learning services with enterprise applications through REST APIs and microservice-based architectures to support real-time inference requirements.

• Participated in model governance activities including model documentation, explainability analysis, validation testing, and compliance with organizational AI standards.

• Worked closely with software development, QA, and infrastructure teams to ensure successful deployment and maintenance of production machine learning systems. Environment: Python, SQL, Pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch, Spark, PySpark, Power BI, Tableau, Flask, Fast API, REST APIs, Docker, Kubernetes, MLflow, Git, Jenkins, AWS EC2, AWS S3, PostgreSQL, MongoDB, Agile, Jira

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