Vetted Amazon Bedrock Professionals

“Engineering leader/player-coach with recent hands-on work delivering an agentic AI MVP on Amazon Bedrock (conversational UI + supervisor agent routing between internal knowledge and external sources). Previously drove large-scale data platform cost optimization at Twitter, saving ~$3M–$5M annually, and has owned production incidents end-to-end with a focus on analytics/monitoring improvements and team coaching.”

“AI/LLM engineer with production experience at NVIDIA and Microsoft, including building a RAG-based enterprise knowledge assistant that improved accuracy by 42% and scaled to thousands of queries. Deep in inference optimization (TensorRT-LLM, Triton, quantization, speculative decoding) and MLOps/observability (Prometheus/Grafana, MLflow, LangSmith), plus orchestration with Kubeflow/Airflow across multi-cloud.”

“Player-coach engineer/lead on a high-scale research integrity platform ("Lighthouse") that flags fraud/manipulation signals across ~3M academic manuscripts per year. Owns architecture decisions (ADRs), implements across Go/Java/React services, and introduced NLP (SciBERT embeddings + human-in-the-loop) to assess out-of-context citations while also handling production incidents with a data-consistency-first approach.”

“Built and deployed an LLM-powered platform that turns models into scalable REST/gRPC APIs, focusing on keeping GPU-backed inference fast and stable during traffic spikes. Experienced with AWS orchestration (EKS/ECS/Step Functions), safe model rollouts, and production-grade monitoring/testing for reliable AI agents and workflows.”

“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”

“AWS AI Lab engineer who deployed a production Chain-of-Thought analytical agent for tabular reasoning, emphasizing grounded tool-constrained workflows with schema-validated intermediate outputs. Built robust evaluation/logging with step-level observability to catch regressions across model versions, and has experience scaling distributed LLM training via Slurm + DeepSpeed/FSDP with checkpointing and failure recovery.”

“Built and deployed an AI receptionist product for field-service businesses (HVAC/electrician), including real-time Jobber scheduling integrations and Twilio-based calling. Combines hands-on customer/operator shadowing with strong production engineering (queueing to handle API limits, rigorous testing/mocking, mirrored prod environment) and cross-layer troubleshooting, driving user adoption through review/override workflows.”

“Backend engineer with Amazon experience building large-scale, automated financial/accounting and pricing systems on AWS. Designed a fault-tolerant Step Functions + DynamoDB workflow platform handling 100K+ messages/sec to compute fair values and generate journal entries in under 3 seconds, and led safe API refactors using shadow mismatch testing. Also uncovered a major legacy pricing bug (tax vs non-tax swap) that cut mismatch rates from 5–10% to ~0.5% and materially improved price acceptance/business outcomes.”

“ML/NLP engineer focused on production-grade data and search/recommendation systems: built an end-to-end pipeline that connects unstructured customer feedback with product data using TF-IDF/BERT, Spark, and AWS (SageMaker/S3), orchestrated with Airflow and monitored for drift. Also has hands-on experience with entity resolution at scale and improving search relevance via BERT embeddings, FAISS vector search, and domain fine-tuning validated with precision@k and A/B testing.”

“Software engineer with strong AWS production experience, including an end-to-end historical backfill system exporting ~10PB of CloudWatch logs into a data lake using Step Functions/Kinesis/Lambda/Firehose/Glue. Emphasizes reliability and operability (DynamoDB checkpointing, monitoring dashboards, CI/CD with canary tests) and has also built customer-facing UI work for the Visa Developer Portal using Angular + Spring Boot, plus React/Redux frontend work.”