Vetted Logistic Regression Professionals

“AI/backend engineer who independently built and operated an agentic telecom analytics system end-to-end, using LangGraph and Claude to turn natural language into safe SQL in a regulated environment. He combines startup-speed execution with compliance-minded rigor, citing 95%+ NL-to-SQL accuracy, a 30-minute-to-2-minute workflow improvement, and zero-findings support across three regulatory audit cycles.”

Ritika Sisodiya

Screened

Junior Data Scientist specializing in statistical modeling and machine learning

California, USA2y exp

“AI Researcher with production experience building a real-time computer-vision detection pipeline augmented by an LLM-based verification layer to cut false positives (~78%) and reach ~90% real-world accuracy. Also partners cross-functionally with Product/Sales/Marketing to shape AI feature prioritization and market positioning using analysis and interactive dashboards.”

“Marketing and product-focused analytics candidate with hands-on experience turning messy large-scale data from Hadoop/HDFS, Azure Data Lake, and transaction systems into validated reporting tables. They combine SQL and Python automation with strong metric design, cohort/retention analysis, and stakeholder-friendly dashboards, including a reported 30% query performance improvement and weekly reporting automation.”

“Backend/AI engineer with deep healthcare experience building production Python microservices that turn raw clinical audio into structured notes and insights. They owned systems end-to-end across architecture, launch, monitoring, and incident response, with measurable impact including 40% lower operating costs, 22% better latency, and 99.9% uptime in a regulated environment.”

“Built and deployed a production AI-powered personalized learning platform (Django + FastAPI) featuring an LLM+RAG tutoring assistant and automated grading. Demonstrates strong applied LLM reliability engineering (structured JSON outputs with Pydantic validation, hallucination control via FAISS-based RAG thresholds and refusals) plus scalable async microservice design and Airflow-orchestrated ETL across AWS/GCP.”

Ramya Jonnala

Screened

Principal/GenAI Engineer specializing in LLMs, RAG, and MLOps

Plano, TX9y exp

“Built a production AI-powered university marking system that automates question generation and grading from PDF course materials using a RAG pipeline (S3 + Pinecone) orchestrated with LangChain/LangGraph and deployed on AWS ECS via Docker/ECR and GitHub Actions CI/CD. Addressed a key real-world LLM challenge—grading consistency—by implementing rubric-based scoring, retrieval re-ranking, and standardized context summarization, validated against human instructors.”

“Early-career candidate with academic data science and machine learning project experience, including a YouTube comment sentiment analysis project comparing LSTM and GRU models. Demonstrates a collaborative working style, structured task planning, and careful review of final deliverables in team-based project settings.”

“Cloud infrastructure/DevOps engineer focused on AWS/Azure production environments, with hands-on experience running EKS-based platforms, Terraform-driven IaC, and secure Jenkins/GitLab CI pipelines. Has led real-world migrations from EC2 to Kubernetes using blue/green cutovers and executed multi-AZ failover testing with documented same-day recovery and no data loss; does not have direct IBM Power/AIX ownership experience.”