Vetted Elasticsearch Professionals

“Built and owned Alight's AI-powered Search Summary feature end-to-end, using a RAG pipeline with OpenSearch and Bedrock, and drove a 20% increase in user feedback scores. Stands out for bringing rigorous production evaluation to LLM systems via live LLM-as-a-judge monitoring, and for experience with advanced agentic architectures, hybrid search, and reranking at scale.”

“Backend/product-minded engineer from Ramp with strong travel-tech experience, having built an end-to-end booking platform integrating multiple external providers, policy enforcement, and reporting infrastructure. They also shipped an LLM-powered personalization workflow using embeddings and Google Gemini that cut trip planning time by 22%, and demonstrated strong production reliability instincts through circuit breakers, health checks, and schema-driven normalization.”

“Software engineer with strong end-to-end ownership of search and listing systems (React/TypeScript frontend with Node.js + Spring Boot backends), focused on shipping fast while managing risk via feature flags, testing, and metrics. Demonstrated measurable UX/performance wins (reduced latency and search abandonment) and built internal observability tooling (dashboard + alerts) that improved incident response. Experienced with microservices reliability patterns including idempotency and dead-letter queues.”

“Built a production internal RAG-based Q&A assistant at Huawei for ~4,000 engineers over a 12M-document Elasticsearch corpus, replacing link-only search with synthesized answers and achieving 87% user acceptance while keeping hallucinations under 0.4%. Pairs rigorous offline benchmarking (RAGAS, PR-gated F1 improvements) with human A/B testing and OpenTelemetry-based production monitoring, and also has strong Kubernetes/SRE experience orchestrating 50+ gRPC services with major MTTR and pager-fatigue reductions.”

“ML/NLP engineer with experience at Flatiron Health building a production NLP platform that processed millions of clinical notes, using BERT/BiLSTM-CRF and spaCy to extract and normalize entities from noisy EMR text with oncologist-in-the-loop validation. Also built scalable retail ML workflows (Spark + Kubernetes + feature store caching) and applied vector databases plus contrastive-learning fine-tuning to improve retrieval relevance and recommendations.”

“CTO who ties technology strategy directly to business outcomes, building multi-year roadmaps with measurable ROI. Led major modernization (cloud, data platform, unified API, microservices + CI/CD) delivering 5x faster releases/deployments, 99.8% uptime, and 40% user growth without headcount increases, while scaling engineering from 15 to 80+ in ~18 months.”

“Full-stack engineer with fintech/trading domain experience (Fidelity) and startup SaaS CRM/billing platform work (Zoho), building real-time portfolio analytics and trade-processing systems. Strong in microservices, event-driven architectures (Kafka/WebSockets), and AWS/Kubernetes operations with measurable performance gains (~34–35% latency reduction) and maintainability improvements (~40% faster deployments). Targeting a founding full-stack engineer role in NYC with meaningful equity.”

“Software engineer with strong end-to-end ownership across frontend, backend, data, and infrastructure, including real-time systems (Kafka/Postgres) and observability (Datadog). Built and productionized an AI-native RAG support assistant (OpenAI embeddings + Pinecone) with prompt/guardrail design, achieving 48% agent adoption and 30% faster responses. Experienced in legacy modernization and reliability work using feature flags, event/transaction replay, and rapid embedded delivery.”

“Infrastructure/operations engineer with deep IBM Power/AIX experience (AIX 7.x, VIOS, HMC/vHMC) managing ~15–25 LPARs across production and DR, including live DLPAR changes and structured performance troubleshooting. Also hands-on with PowerHA/HACMP incident recovery and failover testing, plus broader DevOps delivery building Jenkins CI/CD for containerized microservices and Terraform/Ansible IaC across AWS and Azure, and leading Solaris SPARC to x86 migration cutovers.”

“Healthcare data engineer with hands-on ownership of claims/member data pipelines on a cloud analytics platform, spanning batch and streaming ingestion (Airflow/Kafka/Spark/Databricks) through serving for reporting. Emphasizes reliability and data quality via embedded validation, schema-drift detection, deduplication, and operational monitoring/incident response, plus pragmatic CI/CD and observability setup in early-stage/ambiguous projects.”

Jayanti Lahoti

Screened

Junior Full-Stack Software Engineer specializing in AI and cloud-native systems

San Diego, USA2y exp

“Backend/systems-oriented engineer focused on building production-constrained LLM agent workflows that automate repetitive operator tasks via intent/entity extraction, retrieval grounding, and structured action recommendations with human-in-the-loop review. Emphasizes reliability through deterministic orchestration, strict tool/function schemas, observability, and disciplined evaluation/feedback loops, with strong experience handling messy multi-service operational data and idempotent execution.”

Biswajit Dash

Screened

Senior Platform/DevOps Engineer specializing in CI/CD and Observability

Milpitas, CA25y exp

“DevOps engineer focused on CI/CD who built and productionized LLM/MCP-based chat agents integrated into Cisco Webex to help developers troubleshoot PRs and pipelines via GitHub/Jenkins data. Strong in operationalizing agentic systems with observability (OpenTelemetry/Grafana), user-scoped rate limiting, and Kubernetes-based scaling, and has presented demos on agent SDK capabilities and DORA metrics dashboards.”

Zhiwen Zhao

Screened

Junior Data Engineer specializing in cloud ETL and big data platforms

New York, NY3y exp

“Data engineer focused on transit/transportation datasets, building Spark-based pipelines that ingest from Oracle/APIs, apply PySpark data-quality fixes, and publish star-schema fact tables to Azure Data Lake. Experienced troubleshooting complex Spark failures (using checkpointing to manage long lineage) and operating Airflow-driven backfills and GitLab CI deployments for production DAGs.”

“Built internal production LLM tools for engineering and support, including a customer-health assistant and a RAG-based incident explainer grounded in logs, metrics, and deploy data. Stands out for combining strong GenAI safety/evaluation practices with pragmatic backend engineering, delivering measurable impact like a 40% drop in data-help requests and answers in seconds instead of minutes or hours.”

“Currently at Berryble AI, this candidate is building an LLM-based real-time interview analysis engine using FastAPI, WebSockets, fine-tuned models, and GCP/Cloud Run. They stand out for using AI and agent workflows pragmatically to accelerate development while keeping human ownership over architecture, security, reliability, and maintainability, and they are also pursuing a master's in applied machine learning.”

“AI/ML engineer with production experience across both classical ML and Generative AI, including a real-time banking fraud detection platform at Deloitte and a RAG-based cybersecurity threat analysis feature at Accenture. Stands out for owning systems end-to-end—from feature pipelines and model tuning through deployment, monitoring, retraining, and API/platform reliability—with measurable impact on fraud accuracy, false positives, and SOC analyst efficiency.”

“Built production-scale dashboards and real-time data systems across fintech and autonomous driving, with hands-on ownership from React frontend architecture through Python/Kafka/Elasticsearch backend pipelines. At Zoox, led development of an automated safety signal attribution platform that replaced manual engineering calculations and surfaced trends through dashboards.”

“Banking infrastructure engineer who owns large-scale IBM Power/AIX (AIX 7.x, VIOS, HMC/vHMC) environments with hundreds of LPARs and deep PowerHA/SAN recovery experience. Also builds modern cloud delivery platforms—Azure DevOps/Jenkins CI/CD and Terraform for AWS/Azure (EKS/AKS, networking, security)—bridging legacy mission-critical systems and cloud-native automation.”

Prashant Singh

Screened

Mid-level Backend/Platform Engineer specializing in data pipelines, reliability, and AI-assisted ingestion

8y exp

“Backend engineer who built and scaled a blockchain-based e-voting platform at early-stage startup Elemential Labs, balancing decentralization with real-world operability by centralizing control-plane components while keeping the ledger immutable. Has hands-on experience migrating high-throughput ingestion from Kafka to AWS Kinesis with parallel cutover, strengthening data integrity and read-after-write consistency (Elasticsearch), and hardening pipelines against silent data-quality failures via anomaly detection and self-healing automation.”

“Builder/technical leader who was brought into Finicity to turn a credit-improvement concept into a viable product—architected, staffed, and launched what became Experian Boost. Delivered a major North American product launch in ~6 months, scaling to ~50,000 new users per day at launch and solving complex ML classification and distributed processing/order-of-operations challenges on AWS.”

“Full-stack engineer focused on high-throughput document/financial data platforms, building Angular/React front ends and Spring Boot microservices with Python/Flask services for heavy processing. Experienced in designing non-blocking, asynchronous workflows (Celery/RabbitMQ) and deploying containerized systems to AWS ECS with auto-scaling and CloudWatch monitoring.”

“CTO/AI executive with repeated 0-to-1 leadership and founder experience across banking software, cloud, and fintech. Most recently, as a fractional CAIO, built a 50+ person team and launched 3 products in 9 months generating $10M+ new revenue; previously founded Trilogy (200+ clients in 7 countries) and created cloud tech that helped drive a $35M acquisition by VMware.”