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Vetted Machine Learning Engineers in Washington
Pre-screened and vetted in Washington.
Staff Machine Learning Engineer specializing in search, ranking, and LLM systems
Senior AI/ML Engineer specializing in LLMs, recommendation systems, and ML platforms
Staff Machine Learning Scientist specializing in NLP, LLMs, and Generative AI
Deepak Guntreddi
Mid-level Full-Stack Developer specializing in Java/Spring Boot and React
“NVIDIA engineer who built and shipped a production LLM-powered enterprise knowledge system (summarization, transcription, and Q&A) that cut document retrieval time ~30%. Deep hands-on experience with RAG (FAISS/Pinecone), GPU-accelerated microservices on AWS, and reliability/safety practices (Guardrails AI, prompt A/B testing, canary releases) plus strong MLOps orchestration across Airflow, Step Functions, and Kubernetes GitOps.”
Mid-level AI/ML Engineer specializing in LLMs, NLP, and MLOps
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and scalable inference
Mid-level Security Engineer specializing in application security, IAM, and AI red teaming
Mid-level Machine Learning Engineer specializing in real-time recommender systems and MLOps
Senior Machine Learning Engineer specializing in GenAI, NLP, and recommendation systems
Junior Software Engineer specializing in AWS cloud infrastructure and ML systems
Mid-Level Software Engineer specializing in cloud platforms, ML/GenAI, and distributed systems
Principal AI/ML Engineer specializing in personalization, NLP, and MLOps
Mid-level Machine Learning Engineer specializing in GenAI, LLM agents, and MLOps
Yashwanth J
Mid-level Software Engineer specializing in LLM agentic AI and full-stack systems
“Full-stack engineer at Bank of America who built and iterated a real-time transaction monitoring/fraud detection system processing 50K+ daily transactions, improving latency (25%), dashboard performance (30%), and reducing manual investigation time (40%) while meeting PCI DSS via OAuth2 and RBAC. Also built a scalable ETL pipeline for messy financial data with strong reliability/observability (ELK, retries, DLQ), boosting data integrity from 87% to 99% and sustaining 99.8% uptime.”
Mid-level AI/ML Engineer specializing in LLM agents, RAG, and cloud-native ML systems
Mid-level AI Backend Engineer specializing in LLM applications and scalable ML services
“ML/LLM practitioner with experience at Truveta building an LLM-based evaluation framework; identified non-overlapping evaluator failure modes and proposed an ensemble approach that enabled scaling training data and drove ~5% performance gains across multiple internal projects. Strong focus on robustness to distribution shift (augmentation/domain adaptation/meta-learning) and production reliability via monitoring, drift detection, and safe fallbacks.”
Mid-level Machine Learning Engineer specializing in MLOps and applied AI
Divyam Agrawal
Mid-level Machine Learning Engineer specializing in LLMs and NLP classification systems
“Internship experience building a production RAG+LLM pipeline to map messy card transaction descriptions to merchant brands, including a custom modified-ROUGE evaluation approach for weak/variant ground truth. Improved scalability and cost by moving from a managed LLM endpoint (e.g., Bedrock) to self-hosted vLLM, and orchestrated massive embedding backfills (5,000+ files, 10B+ rows) using an Airflow-triggered SQS + ECS worker architecture with robust retry/DLQ handling.”