Vetted Dashboarding Professionals

“Revenue growth and RevOps veteran (25+ years) relaunching Impactus Growth Advisors, a services firm for early-to-growth-stage startups focused on execution-heavy revenue ops. Has validated tiered service packages through years of fractional consulting and is planning an AI-based Q&A app as an entry point to scale GTM and service delivery, with intent to bootstrap or potentially raise via angels.”

“Built a production AI-powered customer support Q&A system using an internal knowledge base to reduce repetitive ticket work and improve customer satisfaction, with an emphasis on source-backed answers and expert oversight. Also has experience defining deployment services in a microservices architecture and integrating large-scale APIs (including work connected to US HHS/COVID-19).”

“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”

“ML/NLP practitioner with healthcare payer experience at HCSC, focused on connecting messy unstructured clinical notes to structured claims/provider data to improve fraud-analytics workflows. Has hands-on experience fine-tuning transformers in AWS SageMaker, building large-scale embedding search with FAISS, and implementing robust entity resolution using golden datasets, precision/recall calibration, and production monitoring for drift.”

“Software engineer with connected-car/automotive production experience who owned an end-to-end remote door lock/unlock feature and introduced unit testing (GTest) plus rig/simulator validation. Also built and productionized an AI-native AWS cloud cost assistant (Lex + GPT-based LLM + Lambda + RAG/vector DB) with guardrails and achieved 94% evaluation accuracy. Helped replace a third-party solution with an in-house build, saving the company ~€9M.”

Allen Saunders

Screened

Senior DevOps/Solutions Engineer specializing in CI/CD, cloud platforms, and API integrations

San Francisco, California11y exp

“Solutions Architect with 5+ years leading pre- and post-sales engagements, focused on taking complex tooling from test/prototype to secure production through a structured discovery-to-deployment approach. Experienced in LLM workflow troubleshooting using tools like Langfuse/Gopher and in developer enablement via concise, hands-on workshops (e.g., Jenkins on Kubernetes at scale). Has navigated internal and external blockers to drive adoption and keep enterprise deals moving (including a Jenkins sale to Love's).”

Srinandh Reddy

Screened

Mid-Level Software Engineer specializing in backend, cloud, and event-driven systems

Aurora, Illinois5y exp

“Robotics software engineer focused on backend and distributed systems for real-time robot operations, including sensor ingestion, robot state management, and robot-to-cloud communication. Hands-on with ROS/ROS2 integration and real-time navigation debugging, plus production-grade monitoring, CI/CD, and containerized deployments (Docker/Kubernetes) to improve stability and performance.”

“Built and shipped a production LLM-powered email automation agent for procurement that ingests emails/attachments, classifies requests via rules+embeddings+LLM fallback, enriches responses with SAP inventory data, and generates templated replies. Architected it as an event-driven, idempotent Azure Functions/Queues pipeline with schema-constrained outputs, confidence gating, retries/circuit breakers, and Application Insights monitoring—cutting turnaround time from 4–7 days to near real-time while maintaining zero downtime.”

“Forward-deployed, full-stack/platform engineer who owns production features end-to-end across frontend, backend, data, and infrastructure (AWS serverless, Terraform, React). Has modernized critical fintech/payment systems (zero-downtime monolith-to-microservices with Kafka event sourcing) and productionized AI-native support workflows (LLM + RAG on Pinecone) with measurable gains in latency, incidents, CSAT, and support efficiency.”

“Built and deployed a production LLM/RAG system at CVS to automate clinical documents, addressing PHI compliance, retrieval accuracy, and latency; achieved a 35–40% reduction in review effort through chunking and FP16/INT8 optimization. Also has experience translating AI outputs into actionable insights for non-technical stakeholders (sports analysts).”

“Game QA professional with hands-on PC/mobile testing experience on major titles including Call of Duty Mobile and PUBG Lite at Garena Indonesia. Has run full-cycle functional/regression testing plus live ops coordination across multiple regions, using standardized bug/reporting workflows and dashboards to keep distributed teams aligned. Familiar with the intent of console certification-style requirements (UI consistency, error handling, save/load, localization formatting) despite not having direct TRC/XR/LOT submission experience.”

“Infrastructure/DevOps engineer (Geico) focused on AWS and Kubernetes at production scale. Has hands-on experience building secure GitHub Actions CI/CD for EKS, provisioning core AWS infrastructure with Terraform/CDK, and leading end-to-end incident response with post-incident automation to prevent recurrence; no direct IBM Power/AIX/PowerHA experience.”

“Full-stack engineer who built an end-to-end low-code/no-code IDE for creating AI/ML workflows for industrial IoT sensors using Next.js/TypeScript and NestJS microservices. Focused on scaling high-volume sensor dashboards—improved UX and performance via WebSockets, debouncing, pagination, and API payload reduction—validated with profiling tools and user feedback in a startup environment.”

“Consulting-style LLM practitioner who builds enterprise knowledge assistants using RAG and takes them from prototype to production with guardrails, evaluation, and full-stack observability. Experienced partnering with IT and customer-facing teams to demo solutions, build tailored prototypes, and drive adoption through API-based integration.”

Monisha Nettem

Screened

Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps

USA5y exp

“AI/ML engineer with banking domain experience (M&T Bank) who built a production credit-risk prediction and reporting platform combining ML models (XGBoost/TensorFlow) with a RAG pipeline (LangChain + GPT-4) over compliance documents. Delivered measurable impact (≈20% better risk detection/precision, 50% less manual reporting) and productionized workflows on Vertex AI/Kubeflow with CI/CD and monitoring; also implemented embedding-based semantic search using FAISS/Pinecone.”

PremKumar Gandla

Screened

Mid-level AI/ML Engineer specializing in MLOps, NLP, and scalable model deployment

Texas, USA4y exp

“Built and deployed a production autonomous AI data analyst agent (LangChain + GPT + Streamlit on AWS) that turns natural-language questions into validated SQL, visualizations, and insights, cutting manual analysis time by ~50%. Emphasizes reliability and MLOps: schema-aware validation/guardrails to prevent hallucinations, scalable large-data processing, and Azure DevOps CI/CD + MLflow for automated deployment and experiment tracking.”

SREYAS GANGJI

Screened

Mid-level Software Engineer specializing in AI/ML backend systems

Chicago, IL4y exp

“AI/data engineer at ZS Associates focused on production-grade agentic systems, FastAPI microservices, and cloud-native ETL/RAG pipelines at significant scale. They’ve built multi-agent validation and diagnostic workflows inspired by their Copilot/KUBEPILOT AI work, supporting 500K+ records per day while improving ML inference performance by ~30% and cutting manual troubleshooting by 60%.”

Dinesh Guguloth

Screened

Mid-level Software Engineer specializing in full-stack cloud-native and AI applications

New York, NY4y exp

“Full-stack engineer with cloud and GenAI experience who has owned production features end-to-end, including a reporting dashboard optimized from 14s to 5s using query/API refactoring and monitored via AWS CloudWatch. Also productionized an OpenAI-powered chatbot using LangChain with prompt design, guardrails, and evaluation via production logs and user feedback, and has led incremental legacy-to-microservices modernization with parallel run to avoid regressions.”

“Backend/data engineer who built a production booking platform on FastAPI microservices (Postgres/Redis/gRPC) and delivered AWS infrastructure spanning Lambda, ECS, SQS, and Glue-to-Redshift analytics. Demonstrated measurable SQL optimization (10 minutes to <40 seconds) and strong operational ownership through monitoring, incident response, and schema-evolution hardening.”

“AI/NLP-focused practitioner who built a zero-/few-shot LLM event extraction system on the long-tail Maven dataset, combining prompt-structured outputs with LoRA/QLoRA fine-tuning and rigorous F1 evaluation. Also implemented entity resolution/data cleaning pipelines and embedding-based semantic search using Sentence-BERT + FAISS, and has healthcare experience delivering a multilingual speech/translation mobile prototype using HIPAA-compliant Azure Cognitive Services.”