Vetted Elasticsearch Professionals

“Backend/data engineer with strong authorization and compliance-domain experience: led a phased migration from a simplistic role model to modern RBAC on a Python serverless stack (Auth0 + AWS Lambda/API Gateway), coordinating changes across 5 repos with extensive manual and automated validation. Previously built and operated custom ETL pipelines (Airflow + Groovy/Java on Spark/YARN/Hadoop) to normalize messy customer email/chat/voice data for NLP-driven financial compliance indicators, including complex email journaling metadata enrichment and large-scale remediation reprocessing after production bugs.”

Deekshit Myakala

Screened

Mid-level Software Engineer specializing in cloud automation and data/ETL platforms

Arlington, Virginia6y exp

“Backend engineer with AWS multi-region production experience building APIs and workflow automation for data center/storage hardware operations (firmware orchestration, maintenance checks, ticketing, dashboards). Also shipped an internal AI chat tool that parses hardware runbooks and incorporates user feedback to retrain the model, and has a strong testing/quality discipline (95%+ coverage) plus database performance tuning via indexing and query monitoring.”

Kevin Allen

Screened

Senior AI/ML Engineer specializing in conversational and generative AI

Austin, TX12y exp

“Built and productionized an LLM-based support assistant end-to-end, including RAG, APIs, monitoring, guardrails, and agent feedback loops. Stands out for translating GenAI prototypes into reliable production systems with structured evaluation, safety controls, and reusable Python infrastructure that improved both support quality and engineering velocity.”

“Backend/distributed-systems engineer with Uber experience building real-time telemetry and safety signal pipelines. Strong in Kafka-based event-driven architectures, low-latency processing under peak load, and production reliability via monitoring, retries, and fallback logic; has Docker/Kubernetes and CI/CD deployment experience.”

“IBM Power/AIX infrastructure engineer who has owned a large enterprise footprint (40 Power8/9 frames, 400+ AIX LPARs) with deep hands-on VIOS/HMC, NIM, performance tuning, and PowerHA recovery. Demonstrated high-impact incident response (avoided DB reboot saving ~4 hours; restored clustered services in <20 minutes) plus strong RCA and preventative remediation. Also brings modern DevOps/IaC experience building GitHub Actions pipelines and Terraform-managed AWS EKS/VPC/RDS/S3 environments.”

“ML/AI engineer with experience at Uber and Scale AI, focused on customer service automation across both classical NLP and generative AI systems. Has owned systems from experimentation through production on AWS, including LLM fine-tuning, RAG optimization, safety evaluation, and internal Python platform tooling that improved consistency and engineering velocity.”

“Built a production AI-powered workflow automation system at Netflix that integrated OpenAI and LangChain with FastAPI services on AWS, cutting roughly 320 hours of manual operational effort. Brings a mix of full-stack product development and practical AI systems experience, with strong attention to reliability, maintainability, and non-technical user adoption.”

“Gameplay engineer with hands-on ownership of a real-time C++ combat ability system, including diagnosing and eliminating large-scale combat frame spikes by refactoring hit detection to an event-driven, animation-notify approach (cut collision checks ~80%). Also implemented UE5 networked abilities (dash) with client-side prediction and server-authoritative reconciliation, plus projectile ballistics validated through debug spline visualizations and unit tests.”