AI/ML Engineer - Generative AI, LLMs, MLOps Expert

Mounika S AI/ML Engineer

************@*****.*** +1-816-***-****

PROFILE SUMMARY

AI/ML Engineer with 5+ years of experience designing, developing, and deploying production-grade machine learning and AI systems across healthcare, finance, and industrial domains.

Strong hands-on experience with Generative AI, LLMs, RAG pipelines, embeddings, and multi-agent systems, delivering intelligent automation and decision-support solutions.

Proven ability to build scalable ML APIs, real-time inference pipelines, and cloud-native AI platforms supporting high availability and low-latency use cases.

Experienced in MLOps, CI/CD, model monitoring, drift detection, and explainable AI, ensuring reliable and compliant ML systems in production.

Extensive experience working across AWS, GCP, and Azure, supporting the complete ML lifecycle from data ingestion to deployment and monitoring.

Experience collaborating with Product Managers, Data Engineers, and Platform Teams to deliver production-ready AI/ML solutions aligned with business KPIs.

Hands-on exposure to LLMOps practices including Prompt Versioning, Evaluation Pipelines, Latency Optimization, and Cost Monitoring for large-scale LLM deployments.

TECHNICAL SKILLS

Programming Languages: Python, R, Java, Scala, C++, SQL

Machine Learning & Deep Learning: Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, LightGBM, H2O.ai, OpenCV

Model Architectures: CNNs, RNNs, Transformers, GANs, Autoencoders, K-Means, DBSCAN, GMMs

Generative AI & LLMs: LangChain, Hugging Face Transformers, OpenAI API, RAG, Embeddings, Prompt Engineering, LLM Evaluation, Multi-Agent Systems, Agent Orchestration

Vector Databases: FAISS, Pinecone

Big Data & Streaming: Apache Spark, Hadoop, Kafka, Flink, Hive, HBase

Cloud Platforms: AWS (SageMaker, Lambda, Redshift), GCP (Vertex AI, BigQuery, Dataflow), Azure (ML Studio, Databricks)

MLOps & DevOps: MLflow, Docker, Kubernetes, Airflow, Jenkins, CI/CD Pipelines, Feature Stores, Model Versioning

APIs & Deployment: FastAPI, Flask, REST APIs, gRPC, Streamlit

Databases: MySQL, PostgreSQL, MongoDB, Cassandra, DynamoDB, Snowflake

Data Processing & Analysis: Pandas, NumPy, Dask, NLTK, spaCy

Visualization & BI: Tableau, Power BI, Plotly, Dash, Matplotlib, Seaborn

Responsible & Explainable AI: SHAP, LIME, Fairlearn, What-If Tool, Model Interpretability

Production ML & Reliability: Model Monitoring, Drift Detection, Real-Time ML Pipelines, Low-Latency Inference, ML Reliability Engineering, Model Governance

Compliance: HIPAA, GDPR, CCPA

Additional Skills: LlamaIndex, LangGraph, AutoGen, CrewAI, Semantic Kernel, Prompt Templates, Function Calling, Tool Calling, Prompt Engineering, Kubeflow, KServe, Model Serving, Canary Deployments, Blue-Green Deployments, A/B Testing, CI/CD Pipelines, Prometheus, Grafana, OpenTelemetry, LLM Observability, Latency Monitoring, Cost Monitoring, Databricks, Delta Lake, Feature Stores

WORK EXPERIENCE

Machine Learning Engineer Nov 2024 – Current

Emerson Electric Co - St. Louis, Missouri

Responsibilities:

Designed and deployed machine learning models for predictive maintenance, anomaly detection, and energy optimization, improving reliability of industrial systems using TensorFlow and PyTorch.

Built end-to-end MLOps pipelines using Airflow and MLflow, enabling automated training, model versioning, and continuous performance monitoring.

Developed LLM-based AI copilots using LangChain and Vertex AI to summarize sensor data and support real-time operational decision-making.

Implemented RAG pipelines integrating FAISS and Pinecone to enable enterprise knowledge search and contextual analytics across IIoT platforms.

Designed and deployed multi-agent AI systems where agents autonomously selected tools, retrieved context, and executed actions based on real-time signals.

Deployed scalable ML services using FastAPI, Docker, and Kubernetes, integrated with CI/CD pipelines for reliable production releases.

Applied model explainability, drift detection, and monitoring techniques to ensure transparency, stability, and long-term performance of deployed models.

Collaborated closely with cross-functional engineering teams to integrate AI solutions into existing manufacturing and cloud platforms.

Implemented LLM evaluation frameworks to measure response quality, hallucination rates, latency, and cost across production GenAI systems.

Optimized model inference pipelines using batching, caching, and prompt optimization techniques to improve performance and reduce operational costs.

Environment: Python, TensorFlow, PyTorch, Spark, Airflow, MLflow, LangChain, RAG, FAISS, Pinecone, GCP, Docker, Kubernetes

AI Engineer Oct 2023 – Oct 2024

Centene Corporation - St. Louis, Missouri

Responsibilities:

Developed machine learning models with TensorFlow and MySQL for risk stratification, fraud detection, and population health analytics, enabling earlier identification of high-risk patients.

Built HIPAA-compliant Generative AI solutions using OpenAI API and LangChain to automate patient summaries and clinical insights.

Implemented vector search and retrieval pipelines using FAISS and Pinecone to improve explainability and data accessibility for care teams.

Engineered real-time data pipelines using Spark, Kafka, Flink, and Airflow to support streaming analytics and fraud detection use cases.

Designed and maintained CI/CD workflows using Jenkins and GitLab CI, ensuring reliable ML deployments in regulated environments.

Applied NLP techniques to extract insights from unstructured clinical and claims data, improving automation and decision accuracy.

Built dashboards using Power BI and Tableau to visualize model outputs and operational KPIs for business stakeholders.

Partnered with data engineering and platform teams to productionize ML and GenAI models using standardized deployment, monitoring, and governance practices.

Applied Responsible AI principles including fairness, explainability, and regulatory compliance across healthcare AI systems.

Environment: Python, Spark, Kafka, Flink, Airflow, LangChain, OpenAI API, FAISS, Pinecone, AWS, MLflow, Generative AI, LLMOps, Agentic AI Systems, AI Platform Engineering, Model Deployment & Monitoring, Responsible AI

AI/ML Engineer Mar 2022 – Jun 2023

BNP Paribas - Mumbai, India

Responsibilities:

Developed and deployed ML models for credit risk assessment, fraud detection, and customer segmentation, improving financial decision accuracy.

Built scalable batch and streaming pipelines with Spark, Kafka, and Airflow to process high-volume transactional data, reducing data latency.

Designed agent-based AI systems enabling autonomous document retrieval, reasoning, and summarization across enterprise datasets.

Implemented real-time anomaly detection using Kafka and Flink, supporting low-latency risk monitoring systems.

Containerized ML services using Docker and Kubernetes, ensuring scalability and fault tolerance in cloud environments.

Collaborated with compliance and risk teams to ensure models met regulatory and explainability requirements.

Environment: Python, TensorFlow, PyTorch, Spark, Kafka, Airflow, Docker, Kubernetes, AWS.

Data Engineer Jun 2019 – Feb 2022

Cencora - Hyderabad, India

Responsibilities:

Developed ARIMA, SARIMA, and Prophet time-series forecasting models in Python (pandas, statsmodels) to predict pharmaceutical demand, which reduced inventory overstock by improving planning accuracy.

Developed real-time analytics pipelines using Kafka and Spark, enabling rapid detection of supply-chain disruptions.

Designed and deployed ML-powered microservices using Flask, Docker, and Kubernetes to support healthcare analytics workflows.

Managed ML lifecycle using MLflow, ensuring reproducibility, version control, and performance tracking.

Applied statistical analysis and data preprocessing techniques to improve data quality and model accuracy across datasets.

Environment: Python, Spark, Kafka, MLflow, Docker, Kubernetes, SQL, NoSQL.

EDUCATION

Masters of Science - Computer science

University of Missouri Kansas City- Kansas city, MO

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