Vetted Machine Learning Engineers in Pennsylvania

“CMU IoT coursework project builder who implemented an end-to-end TinyML gesture recognition system on a Particle Photon + ADXL345, streaming data via MQTT/Node-RED to a real-time Node.js frontend and deploying a quantized logistic regression model on-device. Also explored multi-drone coordination, implementing leader-follower offset control and a pivot/arc turning strategy to avoid collisions, and brings practical Docker/Kubernetes plus CI/CD workflow experience from internships.”

“Built and shipped a production multi-agent, voice-based conversational assistant for older adults’ daily health management using Vertex AI, FastAPI, Firebase/Firestore, and Cloud Run, with a custom cross-session memory design to keep responses context-aware at low latency. Also partnered with caregivers/elderly users and health officials, translating needs into workflows and explaining HIV risk predictions with SHAP and dashboards.”

“Built and shipped a production-grade GenAI Fraud & Compliance Investigation Copilot for a large US bank, integrating OCR docs, structured data, and prior case history to generate grounded, regulator-friendly summaries and red-flag highlights. Demonstrates strong end-to-end LLM systems engineering (LangGraph/LangChain, hybrid retrieval with FAISS+BM25, guardrails/citations, streaming/latency optimization) plus rigorous evaluation and close partnership with compliance stakeholders.”

“AI/ML Engineer with 4+ years of experience (Capital One, Odin Technologies) and a master’s in Data Analytics (4.0 GPA) who has deployed LLM/RAG systems to production for compliance/risk and document review. Strong in orchestration and MLOps (Airflow, Kubernetes, MLflow, GitHub Actions) and in tackling real-world LLM constraints like latency, context limits, and data privacy, with measurable impact (20%+ manual review reduction; 33% faster release cycles).”

“AI/ML manager who has deployed production NLP in healthcare—mining unstructured clinical notes and combining them with structured patient data to predict readmissions, with strong emphasis on data alignment and terminology normalization. Also experienced operationalizing ML with Airflow/MLflow and AWS Step Functions/SageMaker, plus stakeholder-facing Power BI dashboards (e.g., marketing customer segmentation).”

“Built and productionized a clinical NLP + patient risk stratification platform at Dermanture, combining Spark/PySpark pipelines with BERT/BioBERT for entity extraction and text classification and downstream risk models in TensorFlow/scikit-learn. Experienced running regulated, auditable ML workflows with Airflow and AWS SageMaker, emphasizing data validation (Great Expectations), drift monitoring, and explainability (SHAP) to drive clinician trust and adoption.”

Lakshmi Meghana

Screened

Mid-level AI/ML Engineer specializing in production ML, MLOps, and NLP

Bristol, PA4y exp

“Built and deployed a transformer-based clinical document classification system that processes unstructured clinical notes in a HIPAA-compliant healthcare setting, served via FastAPI on AWS and integrated into an Airflow/S3 pipeline. Demonstrates strong end-to-end MLOps skills (data quality remediation, low-latency inference optimization, monitoring with MLflow/CloudWatch) and effective collaboration with clinicians to drive adoption.”

“Co-op engineer who owned and shipped a Python/Flask backend for automating architecture reviews and system metadata processing, including ingestion from multiple internal APIs, RBAC, testing, and deployment. Has hands-on Kubernetes + GitOps (ArgoCD) experience, built Kafka-based real-time ingestion, and supported a cloud-to-on-prem migration with phased cutover, smoke tests, and performance tuning.”