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Senior Python Generative AI Engineer with 9+ years of experience building scalable AI, machine learning, data engineering, and cloud-native solutions across banking, insurance, healthcare, and government. Deep expertise in enterprise GenAI, agentic AI, and RAG (AWS Bedrock/OpenAI, LangChain/LangGraph), with production-grade security, monitoring, and MLOps/LLMOps. Experienced in designing secure microservices and data platforms using Python, FastAPI, SQL, PySpark, Kafka, Airflow, and ETL/ELT pipelines on AWS. Skilled in deploying and operating ML/LLM systems with Docker, Kubernetes, CI/CD, IAM/OAuth/JWT/RBAC, and observability tooling (CloudWatch/Prometheus/Grafana/ELK/Langfuse), improving performance, reliability, and data quality.…Senior Python Generative AI Engineer with 9+ years of experience building scalable AI, machine learning, data engineering, and cloud-native solutions across banking, insurance, healthcare, and government. Deep expertise in enterprise GenAI, agentic AI, and RAG (AWS Bedrock/OpenAI, LangChain/LangGraph), with production-grade security, monitoring, and MLOps/LLMOps. Experienced in designing secure microservices and data platforms using Python, FastAPI, SQL, PySpark, Kafka, Airflow, and ETL/ELT pipelines on AWS. Skilled in deploying and operating ML/LLM systems with Docker, Kubernetes, CI/CD, IAM/OAuth/JWT/RBAC, and observability tooling (CloudWatch/Prometheus/Grafana/ELK/Langfuse), improving performance, reliability, and data quality.

Sabeehah Mohammed

AI Engineer, Data Engineer, Programmer, +2

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Senior Python Generative AI Engineer with 9+ years of experience building scalable AI, machine learning, data engineering, and cloud-native solutions across banking, insurance, healthcare, and government. Deep expertise in enterprise GenAI, agentic AI, and RAG (AWS Bedrock/OpenAI, LangChain/LangGraph), with production-grade security, monitoring, and MLOps/LLMOps. Experienced in designing secure microservices and data platforms using Python, FastAPI, SQL, PySpark, Kafka, Airflow, and ETL/ELT pipelines on AWS. Skilled in deploying and operating ML/LLM systems with Docker, Kubernetes, CI/CD, IAM/OAuth/JWT/RBAC, and observability tooling (CloudWatch/Prometheus/Grafana/ELK/Langfuse), improving performance, reliability, and data quality.…Senior Python Generative AI Engineer with 9+ years of experience building scalable AI, machine learning, data engineering, and cloud-native solutions across banking, insurance, healthcare, and government. Deep expertise in enterprise GenAI, agentic AI, and RAG (AWS Bedrock/OpenAI, LangChain/LangGraph), with production-grade security, monitoring, and MLOps/LLMOps. Experienced in designing secure microservices and data platforms using Python, FastAPI, SQL, PySpark, Kafka, Airflow, and ETL/ELT pipelines on AWS. Skilled in deploying and operating ML/LLM systems with Docker, Kubernetes, CI/CD, IAM/OAuth/JWT/RBAC, and observability tooling (CloudWatch/Prometheus/Grafana/ELK/Langfuse), improving performance, reliability, and data quality.

Available to hire

Senior Python Generative AI Engineer with 9+ years of experience building scalable AI, machine learning, data engineering, and cloud-native solutions across banking, insurance, healthcare, and government. Deep expertise in enterprise GenAI, agentic AI, and RAG (AWS Bedrock/OpenAI, LangChain/LangGraph), with production-grade security, monitoring, and MLOps/LLMOps.

Experienced in designing secure microservices and data platforms using Python, FastAPI, SQL, PySpark, Kafka, Airflow, and ETL/ELT pipelines on AWS. Skilled in deploying and operating ML/LLM systems with Docker, Kubernetes, CI/CD, IAM/OAuth/JWT/RBAC, and observability tooling (CloudWatch/Prometheus/Grafana/ELK/Langfuse), improving performance, reliability, and data quality.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Beginner

Work Experience

Senior Python Generative AI Engineer at Discover Bank

December 1, 2024 - Present

Built an enterprise Generative AI Financial Knowledge Assistant to support fraud investigations and compliance workflows. Designed secure FastAPI/Pydantic microservices and REST APIs connecting banking applications to AI services. Implemented S3-to-ingestion pipelines using AWS Glue, PySpark, and Pandas, reducing document processing time by 45% and improving downstream data quality. Developed OCR and document preprocessing for scanned PDFs using PyPDF/pdfplumber and Unstructured; built chunking, embeddings, metadata enrichment, and semantic search pipelines with Pinecone/FAISS/ChromaDB for low-latency retrieval. Implemented RAG using LangChain, AWS Bedrock, OpenAI APIs, and prompt engineering to generate grounded answers and reduce hallucinations. Created agentic AI workflows using LangGraph (state management, conditional routing, multi-step reasoning) for fraud investigation, compliance validation, and knowledge retrieval. Integrated contextual access using SQL/PostgreSQL/Athena/Reds

Senior Data AI/ML Engineer (Python) at Selective Insurance

August 1, 2023 - December 1, 2024

Partnered with claims and underwriting stakeholders to define integration strategies and scalable data solutions for insurance risk and claims analytics. Architected ETL/ELT pipelines using Python, AWS Glue, PySpark, and Apache Spark to standardize policy, claims, billing, and customer data. Built reusable ingestion frameworks for S3 and REST API sources (JSON/CSV) and developed transformations for cleansing, validation, enrichment, reconciliation, and consolidation. Designed analytical data models in PostgreSQL, Amazon Redshift, and Snowflake using CTEs, window functions, indexing, and query optimization. Implemented Apache Kafka streaming for policy updates and claims events to enable near-real-time synchronization. Automated orchestration with Airflow/Lambda/EventBridge and built secure FastAPI services exposing curated datasets to reporting and downstream applications. Optimized Spark performance (partitioning/caching/broadcast joins/execution tuning) reducing batch processing tim

Applied Data Machine Learning Engineer at UPMC Health Plan

February 1, 2021 - July 1, 2023

Collaborated with clinicians and care management teams to translate patient-risk requirements into scalable ML solutions. Consolidated demographics, claims, lab, pharmacy, and provider data using Python/SQL/REST APIs and PySpark on S3. Prepared training datasets with cleansing, missing-value handling, transformations, and feature engineering. Conducted EDA and statistical analysis using Matplotlib/Plotly. Trained predictive models using scikit-learn, TensorFlow, PyTorch, and XGBoost to identify high-risk patients and predict hospital readmissions. Improved performance via feature selection, hyperparameter tuning, and cross-validation. Evaluated models using precision/recall/F1 and ROC-AUC for reliability and clinical relevance. Deployed real-time prediction services with AWS SageMaker, FastAPI/Flask, and MLflow. Automated retraining/validation/deployment workflows using Airflow/Lambda/EventBridge. Monitored with CloudWatch/Prometheus/Grafana/ELK and improved prediction accuracy by 20%

Senior Python Data Engineer at State of Indiana

February 1, 2019 - September 1, 2021

Consolidated citizen, taxation, healthcare, employment, and public-service data from multiple sources using Python/REST APIs (JSON/XML) and AWS S3 for statewide reporting. Migrated large-scale processing workloads to AWS Glue, PySpark, and Apache Spark to improve scalability and support enterprise analytics. Standardized ETL workflows using Python/SQL/PySpark with cleansing, validation, transformation, and reconciliation controls. Integrated PostgreSQL, Snowflake, Amazon Redshift, and internal applications via REST APIs for secure reporting access. Automated recurring workflows with Airflow, Lambda, EventBridge, and shell scripting, reducing manual operational effort. Optimized SQL and production workloads, reducing processing time by 30%. Supported secure deployments using Docker/Jenkins/GitHub Actions CI/CD and IAM/RBAC/encryption in alignment with government security standards.

Python Data Engineer at InfraSoft Tech

June 1, 2017 - December 1, 2018

Created Python and REST API integrations (JSON/XML/Oracle) to extract and process customer, transaction, and account data from banking applications. Built ETL pipelines using Python/SQL/Apache Spark/PySpark to cleanse, validate, transform, and consolidate financial datasets. Implemented secure data exchange via REST APIs (including Java Spring Boot). Maintained batch processing using Spark/PySpark/Hadoop/HDFS for reliable execution and data availability. Optimized Oracle and MySQL SQL queries, stored procedures, views, and indexing strategies to improve reporting performance. Implemented Apache Kafka messaging for near-real-time synchronization of transaction and payment data across downstream applications. Troubleshot pipeline failures, validated data integrity, and supported Linux/Git/shell-scripted deployments. Reduced manual reporting effort by 25% through Agile delivery, unit/integration testing, and code reviews.