Vetted Observability Professionals

Rubeena Riyas

Screened References

Senior AI Engineer specializing in LLM agents, RAG, and scalable data platforms

7y exp

“ML/data engineer who owned an end-to-end production sales analytics pipeline at 15,000+ user scale, delivering ~50% compute reduction, ~80% faster reporting, and ~$1.2M impact. Also shipped a production RAG-based AI assistant over internal BigQuery/docs with evaluation metrics and safety guardrails, and built shared Python libraries to standardize reliability and accelerate engineering teams.”

“Full-stack engineer (T-Mobile experience) focused on authentication/session-management systems, with hands-on work optimizing token-validation flows and reducing latency by eliminating redundant API calls and adding caching. Brings strong production ownership with observability (Splunk/Grafana), Postgres data modeling/index tuning, and resilient async workflow design (idempotency, retries/backoff, queues).”

“Senior infrastructure and platform operations leader with a career spanning public transportation, financial services, and global enterprise environments. Most recently built a centralized shared-services/NOC model at New Jersey Transit supporting 300+ locations, while also bridging engineering and operations leadership and driving ServiceNow-based operational transformation. Strong fit for VP Platform Operations roles requiring reliability, governance, integration, and post-merger-style standardization across complex multi-site organizations.”

JYOTHI N

Screened ReferencesStrong rec.

Senior Data Scientist specializing in analytics, experimentation, and BI on AWS

Austin, TX7y exp

“Data/ML practitioner focused on healthcare data quality and record linkage: analyzed 10M+ records, built anomaly detection and NLP-driven entity resolution, and automated AWS ETL/validation pipelines (Glue/Redshift/Lambda), cutting data errors by 40% and generating $500k in annual savings. Has hands-on experience with embeddings (Sentence Transformers/spaCy), FAISS vector search, and fine-tuning for domain-specific matching.”

“Backend engineer with hands-on ownership of Python/Flask microservices and recommendation systems across edtech and telecom. Deployed and operated real-time personalization/recommendation platforms on AWS EKS with Jenkins-based CI/CD, GitOps-style declarative configs, and strong observability practices. Has migration experience moving legacy mixed environments to modern containerized Kubernetes and built Kafka pipelines feeding ML services while managing schema evolution.”

“Full-stack engineer with hands-on experience spanning real-time AI products, large-scale payments migration, internal research infrastructure, and open-source ML tooling. Particularly compelling is the mix of low-latency React/Node/TypeScript systems work, zero-downtime migration of 50,000 accounts across 12 regions, and proactive contributions to Kubeflow build and security reliability.”

Sreenaina Koujala

Screened ReferencesStrong rec.

Mid-level Full-Stack & AI Engineer specializing in cloud and intelligent systems

Ashburn, VA8y exp

“Builder with experience across government contracting, engineering automation, and solo AI product development. They architected a serverless AWS pipeline that converted unstructured BIM data into IFC 3D models, built an enterprise internal-data chatbot with auditability and guardrails at Steampunk, and independently launched an AI study platform using Claude. Strong fit for early-stage or ambiguous environments where end-to-end ownership and practical AI systems matter.”

“Backend/data platform engineer with production experience at Walmart and Molina Healthcare, building Python microservices on AWS (EKS + Lambda) for real-time inventory and recommendation systems. Strong in reliability/observability and incident leadership, plus modernizing legacy healthcare workflows and building resilient AWS Glue/PySpark pipelines with schema evolution and data quality controls.”

“AI engineer (2+ years; pursuing an online MS at UIUC) who has shipped an AI-powered voice screening platform end-to-end on GCP with strong production monitoring and measurable hiring-process impact (80% reduction in unqualified pass-through; ~50+ hours saved per role). Also built and deployed an AWS-based context-aware hybrid search system using OpenSearch as a vector store, and has hands-on experience with multi-agent LLM orchestration (ReAct) and structured-output guardrails.”

Ajith P

Screened

Mid-level Backend Software Engineer specializing in AI workflow automation for finance and healthcare

4y exp

“Backend/AI engineer with healthcare domain experience who built a patient journey analytics API (FastAPI/PostgreSQL/Snowflake/Redis) and debugged peak-hour latency down from ~900ms to ~50ms via indexing and query optimization. Shipped an LLM-powered clinical summary/recommendation assistant end-to-end and designed a multi-step risk evaluation agent workflow with safety guardrails against hallucinations and unsafe outputs.”

Daniel Jeong

Screened

Junior Full-Stack Engineer specializing in real-time platforms and AI tools

Cambridge, MA3y exp

“Early-career full-stack engineer with unusual depth in mission-critical environments: helped build a cybersecurity operations platform from scratch as the third engineer and shipped it to the National Election Commission of South Korea. Also worked on defense-focused situational awareness software, combining React/WebGL frontend performance work with backend data transformation for real-time weather and map overlays.”

“Built and productionized an LLM-enabled system visualization web app at Longshot, designed modularly to pivot quickly to a mobile-friendly interface as customer needs changed. Experienced in diagnosing LLM/agentic workflow failures using observability, deterministic replay, and fault-tree root cause analysis. Also delivers developer-focused demos and trainings (including robotics deployment/mapping at Meta) and partners with sales as a technical closer, including for government clients by demonstrating failure modes and system modularity.”

“Infrastructure/DevOps-focused engineer working across Linux-based enterprise platforms that include IBM Power/AIX in a broader OpenShift/Kubernetes and cloud ecosystem. Built Azure DevOps CI/CD for containerized deployments and resolved a production deployment failure by tracing ImagePullBackOff to outdated registry credentials in Kubernetes secrets. Uses Terraform (with modular structure) plus Ansible to provision and standardize production environments with pipeline-based validation.”

“Built and owned an end-to-end agentic RAG chatbot platform for Baptist Health that helped clinicians access policy and clinical documents faster, reducing manual lookup by 80% and delivering about $2M in annual savings. Brings strong healthcare GenAI production experience, including HIPAA-aligned governance, PHI redaction, observability, evaluation, and scalable Python/Kubernetes deployment practices.”

“Sole developer behind BirdieAI, an AI-powered golf booking platform built from the ground up, spanning frontend UX, backend services, AWS infrastructure, and Postgres database management. Worked directly with a cofounder in a startup setting to scope and ship an MVP, then improved production reliability significantly by reducing a key extraction failure from 1 in 15 to 1 in 300 while adding operational safeguards and user-driven product improvements.”

“Robotics ML/software engineer focused on Vision-Language-Action control for 7-DoF robots, replacing tokenized action decoding with continuous regression heads (including a logit-weighted expectation approach) to improve stability and real-time behavior. Strong in ROS1/ROS2 systems integration and debugging closed-loop manipulation issues via latency instrumentation, QoS-aware distributed messaging, and sim-to-real validation using Gazebo/Unity, Docker, and CI pipelines.”

“Backend-focused engineer building customer support/order-tracking platforms with Java 17/Spring Boot microservices and a React/TypeScript frontend. Deep experience running event-driven systems on Kubernetes (Kafka, Redis, MySQL) with strong observability (Prometheus/Grafana/Splunk), SLOs, and safe deployment practices (feature flags, canaries). Also built an internal monitoring/debugging dashboard that consolidated metrics and logs for on-call engineers and was adopted by other teams to speed incident response.”

“Built and deployed a production RAG-based LLM Q&A and summarization platform for internal documents, emphasizing grounded answers with structured prompting and citations to reduce hallucinations. Experienced orchestrating end-to-end LLM workflows with LangChain plus cloud pipelines (Azure ML Pipelines, AWS), and runs iterative evaluation using both metrics (accuracy/hallucination/latency/cost) and real user feedback to drive reliability.”

Alp Komban

Screened

Junior Machine Learning Engineer specializing in computer vision for medical imaging

Mountain View, CA2y exp

“Applied ML/LLM practitioner working in healthcare-facing products, using RAG and LoRA fine-tuning on medical data and implementing production monitoring (confidence scoring) for clinician oversight. Has hands-on experience debugging agentic/LLM pipelines (including OCR preprocessing fixes) and regularly delivers technical demos to doctors, investors, and conferences—contributing to adoption and even helping close a funding round through end-to-end pipeline walkthroughs.”

“Industrial AI/ML practitioner with experience deploying real-time monitoring and anomaly detection in a regulated Sanofi vaccine manufacturing facility, including root-cause workflows, logging/alerting, and SOP-aligned validation—achieving ~90% faster anomaly detection. Also built Python/NLP-style automation to accelerate instrumentation & control documentation (~40% faster) and delivered end-to-end predictive analytics for an agri-food operations/distribution client using close operator and leadership feedback loops.”

“Backend/AI engineer at Intuit who built a production AI-powered case assistant for support agents (FastAPI on AWS EKS) combining Postgres case data, OpenSearch retrieval with embedding reranking, and internal LLMs. Improved peak-season reliability by diagnosing P95/P99 timeout spikes and cutting P95 latency from ~800ms to <400ms via composite indexing, keyset pagination, connection pool tuning, and caching, while adding grounded-generation guardrails (evidence packs, confidence thresholds, fallbacks, human-in-the-loop).”

“Backend/platform engineer who owned high-volume Java/Spring Boot microservices on AWS (Kafka + RDS/DynamoDB) and has hands-on experience debugging complex production latency incidents across DB, JVM/GC, and async consumers. Also shipped applied AI features for ops, including an LLM-powered log analysis assistant and an incident-response agent with strong safety guardrails (schema-validated tool use, retries/backoff, and human-in-the-loop escalation).”