.png){kind=logo}
Vetted Machine Learning Engineers in Virginia
Pre-screened and vetted in Virginia.
Mid-level AI Backend Engineer specializing in LLM applications and scalable ML systems
Mid-level AI/ML Engineer specializing in GenAI, NLP, and MLOps for finance and healthcare
Mid-level AI/ML Engineer specializing in LLMs, RAG, and risk/fraud modeling
Diwita Banerjee
Mid-level AI/ML Engineer specializing in LLMs, RAG, and enterprise AI
“Built an enterprise RAG-based document intelligence system at Freddie Mac for regulatory and financial documents, helping analysts cut search time from hours to minutes while improving retrieval accuracy by ~30%. Stands out for combining LLM product delivery with compliance-grade auditability, production monitoring, and scalable Python/FastAPI service design.”
Mid-level AI/ML Engineer specializing in fraud detection, MLOps, and Generative AI
Senior Machine Learning Engineer specializing in LLMs, NLP, and computer vision
Senior Machine Learning Engineer specializing in MLOps, GenAI, and real-time systems
Mid-level Data Scientist / AI/ML Engineer specializing in NLP, CV, and MLOps in Financial Services
Mid-level Generative AI Engineer specializing in LLMs, RAG, and semantic search
Mid-level Machine Learning Engineer specializing in LLMs and production NLP
Rayyan Alam
Junior Robotics & Machine Learning Engineer specializing in autonomy and RAG systems
“New-grad robotics software engineer with hands-on ROS 2 autonomy experience (Nav2, SLAM Toolbox, AMCL) and a strong track record debugging real-world instability (QoS, lifecycle timing, sensor dropouts). Built an HRI speech system on a Stretch 3 robot with deterministic, context-aware templates to manipulate trust/competence/emotion conditions, and integrated an LLM high-level planner that outputs PDDL for classical task planning and replanning.”
Abhinav Vengala
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
“LLM/agentic systems engineer who built a production "Agentic AI Diagnostic Assistant" for network engineers, using a multi-agent Llama 2 + LangChain architecture with RAG over telemetry/incident data in DynamoDB and confidence-based deferrals to reduce hallucinations. Also has strong MLOps/orchestration experience (Airflow, EventBridge, Spark, Docker, SageMaker/ECS) at multi-terabyte/day scale and delivered multilingual NLP analytics (fine-tuned BERT/spaCy) for support operations through hands-on stakeholder workshops.”
Junior AI Software Engineer specializing in Android ML and LLM-powered recommendations
Senior ML Engineer specializing in AI platforms for healthcare and FinTech
Mid-level Software Engineer specializing in Full-Stack and GenAI/ML platforms
Senior Machine Learning Engineer specializing in GenAI and LLM-powered systems
HemaSri Perumalla
Mid-level AI/ML Engineer specializing in fraud detection and healthcare predictive analytics
“ML/AI engineer with production experience in high-scale banking fraud detection at Truist, building an end-to-end pipeline (Airflow/AWS Glue/Snowflake, PyTorch/sklearn) with automated retraining and Kubernetes-based deployment; delivered measurable gains (22% fewer false positives, 15% higher recall) and reduced manual ops ~40%. Also partnered with clinicians at Kellton to deploy an LLM system for summarizing/classifying clinical notes, improving review time and decision speed.”