Vetted Workflow Orchestration Professionals

Fangjian Xiong

Screened

Junior Machine Learning Engineer specializing in NLP and biomedical entity extraction

Boston, MA2y exp

“Built and deployed a production LLM-powered biomedical knowledge extraction pipeline that processed millions of papers to identify tools/techniques and produce a unified knowledge graph via active learning NER (Prodigy + spaCy transformers) and entity linking (Bio-tools/Wikidata). Addressed hard NLP engineering challenges like WordPiece span-offset alignment and scaled inference over ~1.5M documents using batching/caching, containerized services, async workers, and orchestration with Prefect/Airflow.”

Daniel Jin

Screened

Intern Site Reliability Engineer specializing in Kubernetes, AWS, and observability

New York, NY1y exp

“Backend/data engineering candidate specializing in Python/Flask services and ML-enabled systems, deploying containerized workloads on AWS ECS/EKS with strong observability (Prometheus/Grafana) and PostgreSQL performance tuning. Built multi-tenant architectures with row- and schema-level isolation and optimized a Kubernetes-based Airflow + Spark nightly ETL pipeline for an e-commerce client, improving performance by 250%+ and reliably beating morning reporting deadlines; also contributed to Apache Airflow (SQLAlchemy/PostgreSQL area).”

Sreelekha Vuppala

Screened

Mid-level Data Scientist specializing in Generative AI, MLOps, and cloud data platforms

USA4y exp

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”

“Built both traditional internal tooling and LLM-powered systems during an internship, including a React/Python/AWS calculator onboarding platform and a production-style ROS2 RAG assistant over 10K+ documents. Stands out for combining full-stack delivery, stakeholder coordination, and practical AI reliability work like retrieval tuning, source-grounded answers, and low-confidence fallbacks.”

Supreet Purthpli

Screened

Mid-level AI/ML Software Engineer specializing in cloud-native MLOps and FinTech

San Francisco, CA4y exp

“Software engineer with JPMorgan Chase experience delivering end-to-end fintech features (Next.js/React/Node/Postgres on AWS) and measurable performance gains. Built and productionized an AI-native credit decisioning workflow combining LLMs, vector retrieval, and a rules engine with strong governance (bias checks, auditability, human-in-loop), improving precision and cutting underwriting turnaround time by 40%.”

Ajay Desai

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and backend platforms

USA4y exp

“Built an AI-native legal research platform that automated analysis across 100,000+ dense legal documents, combining LLM workflows, async backend architecture, and conversational retrieval in production. Also brings cross-domain experience in investment-analysis agents and healthcare claims/billing systems, with a strong emphasis on reliability, deterministic orchestration, and safe handling of messy operational data.”

“Built and deployed a production LLM-powered calorie-counting chatbot that turns plain-English meal descriptions into normalized food entities, quantities, and calorie estimates using a hybrid transformer + rule-engine pipeline. Emphasizes reliability with schema/constraint guardrails, confidence-based routing (including embedding similarity search fallbacks), and strong observability/metrics (hallucination rate, calibration, latency, cost). Partnered closely with nutritionists to encode domain standards into mappings and validation logic.”

“AI/LLM engineer with recent production experience at UnitedHealth Group building an end-to-end RAG system over structured EMR data and unstructured clinical notes, including evidence retrieval, GPT/LLaMA-based reasoning, and a validation layer for reliability. Strong in orchestration (Kubeflow/Airflow/MLflow), prompt engineering for noisy healthcare text, and rigorous evaluation/monitoring with gold-standard benchmarking, plus close collaboration with clinical operations stakeholders.”

“Full-stack engineer with strong production ownership who built a "Problem Workspace" coding feature using Next.js App Router + TypeScript, combining Server Components for fast initial render with WebSocket-driven real-time execution updates. Demonstrates deep reliability and data-consistency expertise (idempotency keys, Postgres constraints/indexing, EXPLAIN ANALYZE) and has implemented durable async orchestration (Temporal-style workflows) to reduce failures and timeouts under load.”

“AI/ML engineer who built a production RAG-based internal document intelligence assistant (LangChain + Pinecone) to let employees query enterprise reports in natural language. Demonstrated hands-on pipeline orchestration with Apache Airflow and tackled real production issues like retrieval grounding and latency using tuning, caching, and token optimization, while partnering closely with non-technical business stakeholders through iterative demos.”

“Full-stack engineer who has shipped and owned production analytics dashboards using Next.js App Router + TypeScript, combining server components for data-heavy pages with client components for interactive charts/filters. Also built a Temporal-orchestrated payment reconciliation workflow with versioning, idempotency, and exponential-backoff retries, and has hands-on Postgres query/index optimization using EXPLAIN ANALYZE.”

“Backend/API engineer with ~4 years experience building production services in .NET Core/PostgreSQL/Redis/Docker and optimizing real-world latency issues (claims ~60% response-time improvement). Also built and owned an end-to-end RAG-based AI assistant using Python/FastAPI, OpenAI APIs, and Pinecone, plus agentic workflows with reliability guardrails (retries, confidence thresholds, monitoring). Currently pursuing a master’s degree and targeting a $150k base salary.”

“Built and productionized LLM-driven content intelligence/SEO agents for a high-traffic media platform, automating tagging/summarization/metadata with FastAPI + async orchestration and strict JSON-schema outputs. Demonstrated measurable impact (40% faster publishing, +20% organic traffic in 3 months) and strong reliability practices (offline evals, shadow mode, canaries, fallbacks, idempotency, and monitoring).”

“Data/ML platform engineer with healthcare (Cigna) experience owning an end-to-end pipeline spanning Airflow + Debezium CDC ingestion, PySpark/SQL transformations, rigorous data quality gates, and feature-store/API serving for ML training and inference. Worked at 10+ TB scale and cites a ~30% latency reduction plus stronger reliability via idempotent design, monitoring, and backfill-safe reprocessing; also built pragmatic early-stage data pipelines at Frankenbuild Ventures.”

“LLM/agent engineer with production experience in the insurance claims domain, integrating OpenAI + LangChain into a claims platform to automate unstructured document extraction/classification and cut manual effort by 35%. Built reliable, fault-tolerant AWS/Kubernetes microservices with CloudWatch monitoring plus circuit breakers/retries/fallbacks, and implemented multi-step Spring Boot orchestration with schema validation, confidence gating, and human-in-the-loop handling for low-confidence cases.”

Molli Dinesh

Screened

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

Remote, USA4y exp

“Built an AI-driven insurance policy summarization platform at Marsh, taking it end-to-end from messy PDF ingestion/OCR and custom extraction through LLM fine-tuning and AWS SageMaker deployment. Delivered measurable impact (25% reduction in manual review time, 99% uptime) and demonstrated strong production MLOps/LLMOps practices with Airflow/Step Functions orchestration, rigorous evaluation (ROUGE + human review), and continuous monitoring for drift, latency, and hallucinations.”

SUMIT MAMTANI

Screened

Mid-level Data Scientist specializing in ML, MLOps, and customer analytics

Tempe, AZ4y exp

“ML/NLP practitioner focused on insurance/claims analytics for a large financial firm, working with millions of fragmented structured and unstructured records. Built production-grade pipelines for entity extraction, entity resolution, and semantic search using Sentence-BERT + vector DB, including fine-tuning with contrastive learning (reported ~15% recall lift) and scalable ETL/containerized deployment on Kubernetes.”

Pravalika Kasojjala

Screened

Mid-level AI/ML Engineer specializing in LLM, RAG/GraphRAG, and fraud analytics

Charlotte, NC5y exp

“LLM/agent engineer who has deployed a production internal assistant to reduce employee inquiry resolution time while maintaining regulatory compliance. Experienced with RAG, hallucination risk triage, and graph-based orchestration (LangGraph) for enterprise/banking-style workflows, emphasizing schema-validated, citation-backed, tool-constrained agent designs and tight collaboration with non-technical business/compliance stakeholders.”

Yuvraj Singh Chauhan

Screened

Entry-level AI/ML Engineer specializing in LLMs, RAG, and DevOps automation

Bangalore, India1y exp

“Built and owned a production-scale AI-driven software release/version intelligence platform orchestrated via GitHub Actions that tracks 1000+ upstream repositories and automatically generates SLA-bound JIRA upgrade tickets for hardened container images. Replaced brittle regex/PEP440 parsing with an LLM-based semantic filtering layer plus deterministic validation to handle noisy/inconsistent GitHub tags at scale, with monitoring for coverage, latency, and correctness validated against upstream ground truth.”

Weston Carpenter

Screened

Mid-level Backend Software Engineer specializing in distributed systems

San Francisco, CA6y exp

“Technical/presales engineer with experience at Grubhub and Appen, spanning LLM-adjacent data labeling workflows and production AI troubleshooting. Built an integrations platform at Grubhub and has hands-on experience diagnosing prompt-related AI failures via Splunk, adding JUnit tests and logging to prevent recurrence. Known for shipping customer-specific workflow adaptations (e.g., OCR annotation coordinate transformations for crop/rotation) while keeping timelines intact through iterative delivery and parallelization.”

Sharan Raj Sivakumar

Screened

Senior Software Developer specializing in AI/ML automation and cloud-native systems

New York City, NY6y exp

“ML/MLOps practitioner who built production systems for telecom network analytics, including an automated labeling + multi-label Random Forest solution that cut labeling effort by 90% and sped up RCA. Led an Ericsson auto-deployment platform using Airflow, Azure IoT Hub, Docker, and Celery to orchestrate 120+ containerized ML/rule-based deployments, saving ~80 hours of setup per deployment.”

“Early-career full-stack engineer with startup experience at CLD-9 and Credible Data who built a real-time internal analytics dashboard in Next.js (App Router) with WebSocket-driven sub-second updates and owned monitoring/maintenance post-launch. Demonstrated strong performance and data skills with quantified wins (45% faster load times, 60% better Core Web Vitals) and Postgres query optimization (800ms to 12ms). Completing an MS in CS at CU Boulder (graduating May 2026) and targeting seed-to-Series C roles.”

VINAY KUMAR VIPPALA

Screened

Mid-level Software Engineer specializing in FinTech backend systems

Seattle, WA5y exp

“Full-stack product engineer with hands-on ownership from React UI through Spring Boot APIs and SQL data layers, focused on transaction-heavy fintech workflows. Built both a transaction reconciliation system and a 0-to-1 AI-based anomaly detection workflow at LeisurePay, combining performance-minded frontend engineering with pragmatic product delivery.”