Vetted Workflow Orchestration Professionals

“Built production LLM automations using the Claude API, including a sales enablement workflow that summarizes playbooks and incorporates sales call metadata into strategic one-pagers. Experienced in orchestrating and scheduling data pipelines with SnapLogic, Airflow, and Databricks, and in scaling LLM API calls via parallel/batch processing. Also partnered with HR to deliver prompt-tuned, automated Slack messaging aligned to business tone and acceptance criteria.”

“Built and productionized a telecom-focused RAG assistant by LoRA fine-tuning LLaMA-2 and integrating LangChain+FAISS behind a FastAPI service, with dashboards and a human feedback UI for engineers. Demonstrated measurable impact (≈40% faster document lookup, +8–10% retrieval precision) and strong MLOps rigor via Airflow orchestration, CI/CD, and monitoring for drift and failures.”

“Full-stack engineer who built and owned a production real-time computer-vision inference platform at Credence, spanning Next.js App Router/TypeScript frontend with SSE/WebSocket streaming, a Flask backend, and Postgres analytics. Demonstrated measurable performance wins (70% fewer re-renders; latency cut to ~40–50ms) and strong production rigor (durable orchestration, idempotency, observability, AWS EC2 + CI/CD) with tight post-launch UX iteration based on analyst feedback.”

“Built and shipped LLM/OCR/NLP-driven document-intelligence workflows in operational environments (EnvoyX and UPS), emphasizing production readiness via explicit state-machine orchestration, confidence gates, and human-in-the-loop review. Demonstrated strong business impact in customs brokerage/document ingestion: 50% fewer customs rejects, 30% higher throughput, SLA adherence improved from 71% to 96%, and platform reliability reaching 99.6% with 78% fewer bad-data incidents.”

Jincheng Pang

Screened

Principal Data Scientist specializing in healthcare analytics and medical imaging AI

Sudbury, MA11y exp

“Developed an LLM-driven recommendation agent in Azure Databricks to triage oncology patients and trigger second-opinion case creation using medical claims and EHR data. Uses ICD-10/CPT/J-code features in prompts, embeddings + vector DB similarity, and a backtesting framework emphasizing recall to avoid missing clinically relevant cases while supporting business revenue.”

Prateeksha Ranjan

Screened

Mid-level Software Engineer specializing in embedded AI and full-stack systems

Irvine, California4y exp

“Robotics software engineer who built and owned core navigation components for a TurtleBot in ROS/ROS2 and Gazebo, including an RRT-based planner, waypoint-to-velocity motion planning, and PID trajectory tracking. Demonstrates strong real-time debugging skills (control-loop timing under CPU load), costmap/occupancy-grid tuning, and distributed ROS2 communication design using DDS/QoS, plus Docker and CI/CD automation experience from Keysight.”

Shrey Kumar

Screened

Junior Full-Stack Software Developer specializing in web platforms and AI workflows

Dallas, TX1y exp

“Software engineer who built and shipped “Counsellor AI,” a production LLM-powered academic advising agent for college students using AWS Bedrock and grounded RAG over official university catalogs/policies. Emphasizes reliability through structured JSON outputs, multi-step orchestration with shared state, and strict intake/validation gates to prevent hallucinations and invalid academic plans; also has experience hardening messy telecom operational data pipelines with normalization, permissions, fallbacks, and idempotent patterns.”

silin liu

Screened

Mid-level AI/ML Engineer specializing in LLM agents, RAG, and enterprise ML systems

New York City, NY5y exp

“Built a production multi-agent recommendation/RAG system for internal data analysts to speed up weekly report creation by improving document discovery and automating report/SQL generation. Implemented LangGraph-based orchestration with deterministic agent routing, robust error handling (interrupt/resume), and metadata-driven semantic chunking for diverse PDF/document formats, plus monitoring for latency, throughput, and token/cost efficiency.”

“ML/LLM infrastructure engineer who built and shipped a production internal evaluation + failure-analysis agent (Arthur AI / R3AI context) that orchestrated end-to-end benchmarks with deterministic lineage, regression detection, and root-cause reporting at 5,000+ benchmarks/week. Also built backend observability and data validation systems for analytics pipelines at FullStory processing ~3.4B weekly events, emphasizing schema validation, quarantine fallbacks, and idempotent operations.”

Darshan Patel

Screened

Mid-level Data Engineer specializing in financial and trading data

Sydney, Australia4y exp

“Quant Data Engineer at ASX who is also building FinishKit, a full-stack SaaS that scans AI-generated codebases for bugs and production-readiness issues. Combines React/TypeScript, Supabase/serverless, Fly.io, and Postgres with strong product instincts, rapid iteration, and prior experience building secure multi-tenant data and dashboard systems across enterprise teams.”

“Python backend engineer with experience combining traditional API/microservices development and GenAI integrations, including healthcare claims workflows. Particularly compelling for teams building production AI systems: they pair hands-on work with LLMs, RAG, LangChain-style orchestration, and AWS deployment with a strong emphasis on reliability, security, and engineering discipline.”

“Built end-to-end financial workflow platforms at Citi spanning React frontends, Spring Boot microservices, Kafka, Redis, and Oracle. Particularly compelling for teams needing someone who can modernize legacy systems into real-time architectures—the candidate cites a 48x throughput improvement from a batch-to-Kafka modernization effort.”

“Worked on Sophia the humanoid robot, building production animation pipelines and enhancing human-robot interaction via perception and behavior orchestration. Experienced in stabilizing noisy perception-driven state transitions and designing smooth, user-centered behavioral flows, collaborating closely with artists, animators, and experience designers to translate creative intent into measurable system behavior.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”

“Built and deployed a production LLM/RAG document automation system for business documents (contracts/claim forms) that extracts schema-validated JSON, generates grounded summaries/Q&A, and integrates into transaction systems via APIs. Emphasizes real-world reliability: hallucination controls, layout-aware parsing with OCR fallback, Step Functions-orchestrated workflows with retries/timeouts, and human-in-the-loop review designed in close partnership with operations and claims stakeholders.”

“Built and shipped a production LLM-powered personalized client engagement assistant in the financial domain, balancing real-time recommendations with strict privacy/compliance requirements. Demonstrates strong MLOps/LLMOps depth (Airflow + MLflow, containerized microservices, drift monitoring) and a privacy-by-design approach validated in collaboration with risk and compliance teams.”

“Full-stack engineer (backend-led) with recent experience building enterprise workflow orchestration and billing/payment platforms at Intuit using Java/Spring Boot (WebFlux), Kafka, Postgres/Redis, and React/TypeScript. Has operated at high scale (reported ~1200 RPS during month-end billing) and focuses on event-driven microservices, real-time UI updates via streaming, and disciplined API evolution with contract testing.”

“Built and shipped a production LLM-powered healthcare support agent at UnitedHealthGroup, using LangChain + FAISS RAG on AWS SageMaker with CloudWatch monitoring and human-in-the-loop fallbacks for safety. Strong focus on reliability engineering (confidence gating, retries/timeouts, caching) and continuous evaluation loops; reported ~40% improvement in query resolution efficiency while reducing manual support workload.”

Sai Charan Kolla

Screened

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

TX, USA5y exp

“LLM engineer who built a production document intelligence/RAG pipeline to extract structured data from thousands of unstructured PDFs, cutting manual review time by 60%. Experienced with LangChain and Airflow orchestration plus rigorous evaluation (labeled datasets, prompt testing, HITL review, monitoring) to improve accuracy and reduce hallucinations while partnering closely with non-technical operations stakeholders.”

“Backend/AI workflow engineer who built production-grade orchestration systems for hardware security verification at Silicon Assurance (Nextflow/Python/Postgres) and a multi-agent LLM-driven regulatory code checking system at the University of Florida. Emphasizes reliability: strict plan/execute/verify boundaries, queue-based isolation, and strong observability/auditability with Prometheus/Grafana and persisted prompts/tool calls.”

“Built and shipped a production LLM/RAG risk-case summarization and triage system used by fraud/compliance analysts, with strong grounding controls (evidence-cited outputs and refusal on low confidence). Demonstrates end-to-end ownership across retrieval quality, Airflow-orchestrated indexing pipelines, and compliance-grade privacy (PII redaction, RBAC, encrypted redacted logging, and auditable prompt/model versioning) plus a tight feedback loop with non-technical domain experts.”