Vetted Data Pipelines Professionals

Akshay Katageri

Screened

Mid-level AI Engineer specializing in multi-agent systems and RAG

Jersey City, NJ4y exp

“Built and shipped a production LangGraph-based multi-agent LLM analytics/decision copilot that answers questions across SQL/BI systems and unstructured docs, emphasizing grounded, tool-verified outputs with citations and confidence gating. Deep hands-on experience with orchestration (LangGraph, CrewAI, OpenAI Assistants, MCP) plus real-world latency/cost optimization (vLLM batching/KV caching, speculative decoding, quantization) and rigorous eval/observability. Partnered closely with business/ops stakeholders to deliver explainable reporting automation, cutting manual reporting time by 50%+.”

Tyler Stroud

Screened

Executive Engineering Leader specializing in AdTech and scalable cloud platforms

Miami, FL15y exp

“Engineering leader with experience in small, bootstrapped startups and exposure to VC environments, currently pursuing CTO-level opportunities. Thrives in fast-iterating, high-uncertainty settings and emphasizes data-driven clarity plus strong problem/market validation when evaluating new ventures.”

Shabari Vignesh

Screened

Mid-level Data Engineer specializing in cloud data platforms and AI agents

Santa Clara, CA6y exp

“Data/Backend engineer who has owned end-to-end merchant analytics systems on AWS: orchestrated multi-source ingestion (FISERV/Shopify/Clover) with Step Functions/Lambda, enforced strong data quality gates, and served curated datasets via Redshift and a FastAPI layer. Also built an early-stage Merchant Insights AI agent that converts natural language questions into SQL using OpenAI models, with full CI/CD and observability.”

Harika Reddymalle

Screened

Mid-Level Full-Stack Software Engineer specializing in backend automation and insurance systems

Dallas, TX4y exp

“Full-stack engineer with hands-on production ownership across Angular/.NET/SQL and React+TypeScript/Node/Postgres stacks, including CI/CD and AWS operations (EC2/ECS, RDS, S3, CloudWatch). Delivered an internal insurance document upload and tracking feature end-to-end, adding audit/history and async processing, then validated success through monitoring metrics and reduced support tickets. Comfortable shipping MVPs in ambiguous environments using feature flags, strong validation, and backward-compatible database migrations.”

Wafi Hassan

Screened

Intern Software Tester specializing in SQA automation and backend API validation

Remote1y exp

“Software engineer with medical-domain experience at Philips focused on end-to-end systems integration and reliability, including integrating C# xUnit automated test suites with backend services and CI-driven staging deployments. Also built a Python/scikit-learn typing speed analyzer integrating data collection, model training, and a real-time user interface; interested in transferring this systems/debugging rigor into robotics despite no ROS/robotics project experience yet.”

Snehitha Penumaka

Screened

Mid-level AI/ML Engineer specializing in predictive modeling and cloud ML pipelines

Dallas, TX3y exp

“LLM engineer/data engineer who has deployed production RAG systems for internal-document Q&A, building end-to-end ingestion, embedding, vector search, and FastAPI serving while actively reducing hallucinations and latency through rigorous retrieval tuning and caching. Also experienced in orchestrating cloud data pipelines (Airflow, AWS Glue, Azure Data Factory) and partnering with non-technical business teams to deliver AI solutions like automated document review.”

Valeriy Filin

Screened

Senior Backend/Cloud Engineer specializing in IaC, SaaS platforms, and ML/Computer Vision

Aliso Viejo, CA19y exp

“Backend/infrastructure engineer with experience across API development (FastAPI/MySQL/SQLAlchemy), Kubernetes deployments, and large-scale data processing—built a Dockerized Python pipeline to pre-aggregate ~1B Graylog events for efficient querying. Has enterprise infrastructure automation background at Hewlett Packard Enterprise (Datafabric) using Terraform/Ansible with fail-fast and rollback practices, plus Kafka-based sensor streaming prototypes to Google Cloud with Java workers and autoscaling.”

Rahul Solleti

Screened

Mid-Level Software Engineer specializing in cloud-native microservices and full-stack web apps

United States5y exp

“Backend/platform engineer focused on real-time financial fraud detection and transaction monitoring, building low-latency FastAPI + Kafka systems with strong reliability patterns (DLQs, idempotency) and cloud observability. Has hands-on Kubernetes delivery across AWS EKS and Azure AKS with automated CI/CD and GitOps-style deployments, plus experience migrating legacy C# / Java monoliths to containerized microservices using Terraform/ARM and zero-downtime rollout strategies.”

“Founding/early engineer experience across Asante and a Series A startup (Adgency), shifting from data science/ML into owning production full-stack systems end-to-end. Built core product flows (registration, business profiles, map service), AWS-deployed gRPC microservices with CI/CD, and operated low-latency agent/video ad generation workflows with retries/fallbacks and PostHog-based observability.”

“Built a production LLM-assisted workflow for customer configuration data migrations, combining agentic parsing with deterministic validation and fail-safe pipeline design. Stands out for turning messy ERP and operational data into reliable, repeatable transformations while improving accuracy and cutting manual effort by more than 80%.”

David Alvarado

Screened

Junior Business Analyst specializing in data analytics and BI

Orlando, FL3y exp

“Analytics candidate with insurance domain experience at Chubb, combining strong SQL/Python data engineering for claims reporting with business-facing metric design in Power BI. Also built an MLB game outcome predictor that beat Vegas implied probabilities using public data, showing strong product thinking and applied modeling ability beyond standard BI work.”

“Early-career full-stack builder who created an AI interview-prep platform used by 200+ students, tested it with a 25-student study group, and earned recognition through the CUNY Startup accelerator, including prize money and local college adoption. Has also shipped compliance-sensitive AI products in healthcare marketing and operational tools like invoice approval systems, showing unusual breadth across AI, UX, and backend systems.”

“Full-stack engineer with startup-style ownership across backend, frontend, and AI systems, spanning Java/Spring, React, Node/TypeScript, and LLM-powered retrieval. Shipped a workspace intelligence layer using LangChain, OpenAI, and Pinecone to paying customers, while also improving core product metrics like workspace creation success (+30%), latency (450ms to 280ms), and deployment cycle time (-40%).”

“Built end-to-end AI products across fintech and healthcare, including a real-time loan risk prediction system and a patient feedback insights platform. Stands out for combining full-stack delivery, production ML/MLOps on AWS, and pragmatic human-in-the-loop safeguards; reported a 22% improvement in prediction accuracy.”

“Backend/cloud engineer who built an AWS serverless IoT system that computes Bluetooth beacon locations from telemetry using heavy scientific Python (NumPy/SciPy/pandas) packaged as Dockerized Lambda, integrated with Java microservices and scheduled batch orchestration. Has deep AWS delivery experience (CI/CD with Code* tools, CloudFormation, cost controls) and has led high-severity incident response including CloudTrail forensics and infrastructure recovery after a compromised-keys crypto-mining attack.”

“Senior hands-on full-stack engineer with recent Python/React/TypeScript experience at Terakeet and prior Go-focused backend work at Checkly. Stands out for building and scaling analytics and monitoring platforms in startup-style environments, with strong depth in PostgreSQL performance, containerized deployments, and turning ambiguous business goals into reliable production systems.”

“Backend/data engineer focused on high-scale, event-driven AWS ingestion systems (SQS/Lambda/EKS) processing millions of events per day, with strong reliability patterns (idempotency, DLQs, bounded retries) and deep observability using Datadog distributed tracing. Has delivered Terraform/GitHub Actions CI/CD and improved secret rotation via Secrets Manager + IRSA, plus Glue-based ETL with schema-evolution handling and Postgres SQL optimization (including JSONB/GIN indexing). Candidate is currently living outside the US and states they do not have US work authorization.”

“Software engineer who has shipped both core web platform features (secure user authentication/profile management) and production LLM systems. Built an internal documentation knowledge assistant using a full RAG pipeline (OpenAI embeddings, vector DB, semantic search, reranking) with evaluation loops and a scalable document-ingestion pipeline for PDFs/FAQs, iterating based on metrics and user feedback.”

“Backend engineer with health-tech AI platform experience who designed a modular FastAPI/PostgreSQL architecture supporting real-time user data and swap-in AI workflows. Has hands-on production experience with observability (CloudWatch, structured logging, LangSmith/LangGraph/LangChain tracing), secure auth (OAuth2/JWT, RBAC, RLS), and careful data-pipeline migrations using parallel runs and rollback planning.”

“Built and productionized an LLM-powered financial intelligence and forecasting platform at Northern Trust using a RAG architecture (LangChain + Hugging Face + FAISS) with end-to-end MLOps (Docker/Kubernetes, Airflow, MLflow). Emphasized regulatory-grade explainability (SHAP/Power BI) and hallucination control (retrieval-only grounding), achieving ~30% forecasting accuracy improvement and ~65% reduction in analyst research time, with sub-second inference and 95% uptime on EKS/AKS.”

“LLM/RAG practitioner building clinician-facing AI search and Q&A inside EHR workflows, focused on trust, latency, and safety (grounded answers with citations, PHI controls, encryption/audit logs). Demonstrated real-time incident response for production LLM systems (e.g., fixing a metadata-filter deployment regression to prevent irrelevant results/cross-patient leakage) and strong demo/enablement skills for mixed technical and clinical stakeholders; also shipped a multi-model RAG tool at OrbeX Labs with upload/search/audit features for day-to-day adoption.”

“ML/AI engineer who built and productionized an NLP system at PurevisitX, orchestrating end-to-end ML workflows with Airflow (S3 ingestion through auto-retraining) and optimizing for drift and low-latency inference. Also partnered with Citibank risk teams on a fraud detection model, translating results via dashboards and iterating thresholds based on stakeholder feedback.”

“Built a production LLM-RAG system at McKesson to let internal healthcare operations teams query large volumes of unstructured operational documents via natural language with source-backed answers, designed with HIPAA/FHIR compliance in mind. Demonstrated strong production engineering across hallucination mitigation, retrieval quality tuning, and latency/scalability optimization, using LangChain/LangGraph and Airflow plus rigorous evaluation/monitoring practices.”

“Product-focused full-stack engineer (Spring Boot/Django + React/TypeScript) with deep experience building multi-tenant, enterprise workflow and supply-chain/order-tracking systems. Owned an end-to-end Workflow SLA Breach Prediction & Alerting feature integrating Azure ML for a cloud workflow platform used by ~10,000 enterprise users, and has hands-on AWS operations experience resolving real production latency/scaling incidents via query optimization and Redis caching.”