AI/ML Engineer - Production-Ready Solutions Architect

Sowmya P

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

Mobile: 724-***-****

LinkedIn: www.linkedin.com/in/sowmya-p-a65304274/

AIML Engineer

PROFESSIONAL SUMMARY

Designed AI/ML solutions across healthcare and banking domains, delivering production-ready models, robust pipelines, and measurable business outcomes through disciplined experimentation, monitoring, and governance practices.

Engineered end-to-end workflows in Python with MLFlow, Airflow, and Kubernetes, improving model reliability, deployment repeatability, and operational visibility across secure standardized multi-cloud production environments.

Optimized NLP and computer vision models with fine-tuning, RAG, and evaluation metrics, increasing relevance, reducing hallucinations, and accelerating stakeholder adoption within regulated enterprise workflows.

Integrated cloud services AWS and Azure with Databricks, Spark, and vector databases, enabling scalable data processing, semantic search, secure API delivery, and compliance controls.

Presented technical work clearly to technical and non-technical stakeholders, enhancing cross-departmental collaboration and understanding.

Enhanced enterprise-scale data platforms by executing AI/ML platform engineering and identity security integration, resulting in significant scalability improvements and increased reliability.

Optimized ROI by leveraging Microsoft Copilot and implementing best practices, leading to measurable cost savings and improved operational efficiency.

TECHNICAL SKILLS

Generative AI & LLMs - Large Language Models (OpenAI, Gemini, Claude), AWS Bedrock, LangChain, LangGraph, RAG Architectures, Vector Databases (FAISS, Pinecone), Agents (MCP, AutoGen), Prompt Engineering, Hugging Face Transformers., AI Foundry, Copilot Studio, Microsoft Copilot, Azure OpenAI

Cloud Platforms (AWS & Azure) - AWS (SageMaker, Bedrock, Lambda, Glue, EMR, Kinesis), Azure (Azure ML, Databricks, AKS, Synapse), Serverless Computing., AWS AI/ML

Machine Learning & Deep Learning - PyTorch, TensorFlow, Scikit-learn, Pandas, NumPy, XGBoost, Computer Vision (CNNs, OpenCV, YOLO/OmniParser), Time Series (Prophet, LSTM), Explainable AI (SHAP, LIME)., AI/ML platform engineering

MLOps & DevOps - Docker, Kubernetes (EKS/AKS), Terraform (IaC), MLflow, Apache Airflow, CI/CD

(GitHub Actions, Azure DevOps), Git.

Big Data & Engineering - Apache Spark (PySpark), Kafka, Databricks, SQL, NoSQL (DynamoDB, MongoDB), Data Lakes (S3, ADLS), ETL Pipelines.

Programming Languages - Python (Advanced), R, Scala, SQL, Bash.

NLP Tools & Libraries - SpaCy, NLTK, TextBlob, BERT, RoBERTa

Visualization & Deployment - FastAPI, Flask, Streamlit, Power BI, Grafana, Prometheus. PROFESSIONAL EXPERIENCE

Capital One August 2025 – Present

AI Engineer

Designed credit-risk feature pipelines in Python and Spark on AWS, enabling consistent training datasets for Machine Learning models and faster iteration across enterprise teams.

Engineered RAG-based policy search with LangChain, vector databases, and FastAPI/Flask, reducing analyst lookup time through internal knowledge reviews for daily operations and compliance consistency.

Optimized model serving on Kubernetes with MLFlow tracking and CI/CD, decreasing deployment friction and improving reproducibility for regulated end-to-end production model releases across environments.

Automated data quality checks with Airflow orchestration and Grafana dashboards, detecting anomalies early and proactively preventing downstream scoring issues impacting customer experiences and reporting.

Integrated NewRelic monitoring with Dockerized inference APIs and EventBridge triggers, strengthening observability and ensuring reliable, auditable batch and realtime predictions at distributed service levels.

Enhanced AI/ML platform engineering by leveraging reference architectures AI/ML application development and cloud-based AI services, resulting in improved scalability and performance across enterprise solutions.

Optimized enterprise AI platform by integrating AWS AI/ML and Azure OpenAI, resulting in significant latency reduction and enhanced data processing capabilities. HCA Healthcare May 2024 – October 2024

Machine Learning Engineer

Standardized NLP extraction workflows with LLMs, Prompt Engineering, and evaluation metrics BLEU/ROUGE/Jaccard for text, improving clinical documentation consistency and downstream analytics readiness overall organizationwide.

Configured HIPAA-aligned data pipelines on Azure with Databricks and Spark, enabling secure processing of HL7-like messages and accelerating iterative model experimentation cycles significantly faster.

Validated computer vision classification models in PyTorch and TensorFlow, improving image triage accuracy and supporting faster care-team workflows across departments, facilities, shifts, and clinics.

Analyzed causal inference signals for operational forecasting, improving capacity planning decisions and reducing variation in resource utilization across service lines, locations, units, and wards.

Streamlined semantic search experiences with FAISS and Weaviate vector stores, improving clinician query relevance and reducing time spent locating critical patient context during rounds.

Developed AI-enabled applications utilizing AWS AI/ML and AI Foundry, resulting in increased automation gains and operational efficiency for complex data-driven processes.

Formulated adoption strategy for the oil & gas energy sector within large enterprise consulting environments, resulting in accelerated digital transformation and improved operational effectiveness. Tata Consultancy Services February 2022 – July 2023 AI Engineer

Orchestrated end-to-end MLOps with Kubernetes, Docker, Terraform/CDK, and MLFlow, enabling repeatable deployments, consistent governance, reliable rollback patterns, and cross-team delivery for multi-client AI solutions.

Deployed conversational AI chatbots with LangGraph agents and FastAPI/Flask, improving self-service resolution rates while maintaining secure integrations with enterprise systems, workflows, and knowledge bases.

Instrumented SRE-aligned monitoring with Grafana and NewRelic, improving alert fidelity and reducing incident response time for production AI pipelines, jobs, APIs, and critical services.

Refactored legacy ML pipelines into modular Airflow DAGs with Spark transformations, improving scalability and reducing operational toil during peak data loads, reruns, and backfills.

Secured cloud workloads across AWS and GCP with CI/CD gates and DevSecOps practices, reducing misconfiguration risk and supporting audit-ready delivery across programs and portfolios.

Implemented secure system design focusing on core components and enterprise identity management, resulting in enhanced system reliability and compliance with security standards. Indian Road Survey and Management Pvt Ltd November 2020 – February 2022 Junior AI Engineer

Architected road-asset data ingestion with Python, SQL, and Airflow, consolidating GPS, imagery, and survey feeds into curated marts enabling analytics consistency.

Engineered Spark and Databricks ETL pipelines for spatiotemporal road-condition datasets, improving processing scalability and reducing turnaround for network health reporting deliverables.

Standardized feature-ready datasets by implementing validation checks with Python and SQL, preventing schema drift and improving downstream model training reliability across releases.

Developed baseline anomaly detection for pavement distress signals using scikit-learn and NumPy, surfacing outlier segments and supporting prioritization for maintenance planning.

Automated dataset versioning and experiment tracking with MLFlow and CI/CD, improving reproducibility and enabling gradual transition from reporting pipelines to AIML workflows.

Advanced secure system design in collaboration with Azure OpenAI and Copilot Studio, resulting in fortified data protection and reduced vulnerability exposure across critical applications. EDUCATION

Master's in Computer Science - Auburn University at Montgomery

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