Vetted LangChain Professionals

“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”

“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”

“AI engineer who built an agentic, production-deployed LLM workflow for tobacco violation parsing and automated multi-case creation, using six specialized agents and a human-in-the-loop confidence-threshold routing design. Addressed data privacy constraints by generating synthetic datasets with LLM prompting, and orchestrated reproducible end-to-end pipelines in LangChain with robust testing and evaluation (precision/recall, micro-F1).”

“Backend/platform engineer who built an LLM-driven QA automation system (“mockmouse”) using a Flask orchestration microservice, Socket.IO real-time updates, Redis caching, and strict Pydantic schemas to turn prompts into reliable action graphs and automated browser tests. Has hands-on Kubernetes delivery experience (Docker/Helm/Jenkins) and has supported large migration programs, validating ETL cutovers with 1M+ synthetic records and rigorous output comparisons; also built event-driven monitoring/anomaly detection streaming into Grafana.”

“Backend/AI engineer with production experience in legal tech: built a high-scale licensing/subscription API (FastAPI/Postgres/Stripe) and shipped a RAG-based chatbot for an eDiscovery platform. Designed a robust legal document ingestion workflow that processes thousands of documents into a searchable vector index with clear retry/escalation logic, and has demonstrated measurable Postgres performance wins (200ms to 10ms) using EXPLAIN ANALYZE and composite indexing.”

Prasad Sadineni

Screened

Mid-level AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems

Nashville, TN6y exp

“Building and deploying production in-house, domain-specific LLM chatbots for enterprises that cannot use third-party GPT tools due to internal policies. Focused on reducing latency and improving domain awareness using fine-tuning, continual learning, and advanced RAG/agent retrieval strategies, with experience orchestrating multi-agent workflows via LangChain/LlamaIndex and vector DBs (FAISS, Weaviate, Chroma).”

Shehab mohamed mohamed

Screened

Junior AI Engineer specializing in MLOps, LLMs, and multi-agent systems

Cairo, Egypt2y exp

“ML/AI engineer focused on production-grade systems, with experience building a low-latency multi-agent 'neural concierge' booking platform used across domains like restaurants and hospitals. Also worked on a healthcare computer vision system for nystagmus/eye-movement analysis, showing a mix of scalable LLM infrastructure, MLOps, and safety-conscious medical AI experience.”

Kevin Thomas

Screened

Intern Software Engineer specializing in AI, cloud, and backend systems

Torrance, CA1y exp

“Candidate has internship and graduate-project experience building AI agents, including a production log-analysis assistant using a lightweight agentic/RAG-style workflow with local GPT training and validation against historical logs. They also worked on Android/iOS game build and release processes in a Unity-based robot racing game environment, and highlight measurable LLM outcomes including 80% analysis accuracy, 2-5 second latency, and 50% cost reduction.”

sajda kabir

Screened

Junior Software Engineer specializing in AI, voice, and full-stack product engineering

Kolkata, India2y exp

“Product-minded full-stack engineer from SuperU who built AI voice-agent infrastructure end-to-end, from React/TypeScript campaign UIs to a forked n8n orchestration backend and Postgres multi-tenant data model. Stands out for shipping quickly in ambiguous startup environments, debugging deep reliability issues across layers, and delivering measurable gains like activation rising from 20% to 70%+ and call drops falling below 0.5%.”

Yash Mahajan

Screened

Junior Software Engineer specializing in AI, full-stack development, and applied ML

Fullerton, CA2y exp

“AI/full-stack product builder who has shipped production agentic systems in both customer support analytics and medical claims automation. They combine React/Next.js frontends with Python-based async backends and LLM orchestration, delivering measurable outcomes like 60% cost savings, 40% less manual review, and reducing claims processing from 30 minutes to 20 seconds.”

Parth Shroff

Screened

Entry-level Full-Stack Software Developer specializing in AI and cloud applications

Remote, USA1y exp

“Full-stack engineer who has independently owned and shipped educational web products from concept through launch, including an AI-powered thermodynamics tutoring app and a physics learning platform tied to research publication. Strong in React plus AWS serverless architecture, with a clear pattern of working directly with non-technical stakeholders, iterating from user feedback, and maintaining production systems end-to-end.”

“IBM Power/AIX engineer who has owned a large production estate (20+ Power9/Power10 frames and 400+ LPARs) with vHMC and dual-VIOS HA. Has hands-on incident recovery experience (NPIV/RMC issues, LPM restores) and PowerHA failovers, plus modern DevOps exposure using Terraform on AWS and CI/CD with GitHub Actions/Jenkins (including deploying AI/RAG and vision workloads).”

“Backend/AI engineer who built and refactored a production RAG system over IRS Form 990 filings for 60 nonprofits, using a dual-path architecture (deterministic financial ranking + TF-IDF semantic retrieval) to keep latency sub-2s and reduce hallucinations. Demonstrates strong API craftsmanship in FastAPI (contract-first, OpenAPI-driven) plus production-grade security for multi-tenant systems (JWT, RBAC, Supabase-style RLS) and careful migration practices (feature flags, traffic mirroring, incremental rollout).”

“Systems-focused C++ engineer who built a 32-bit CPU simulator end-to-end (custom ISA, full memory model, fetch-decode-execute loop) and solved tricky recursion/stack-frame correctness issues through heavy instrumentation and tracing. Has strong Linux and user-kernel boundary experience (procfs) plus modern build/test tooling (Docker, CI/CD, pytest), and is confident ramping quickly into ROS/ROS2 despite not having used it directly.”

“Built and productionized LLM-powered workflows that generate contextual insights from structured financial data, including prompt/retrieval design, data standardization, and reliability controls like rate limiting and batching. Also diagnosed and fixed real-time failures in an automated order validation system using logs/metrics, staging reproduction, edge-case handling, retries, and alerting, while supporting sales/customer teams with demos, scripts, and FAQs to drive adoption.”

srikanth tulluru

Screened

Mid-level Product Designer & Design Technologist specializing in design systems and GenAI UX

Remote, USA3y exp

“Enterprise/industrial UX designer focused on making complex, real-time automated systems feel trustworthy and predictable. Has hands-on experience observing operators in logistics/automation environments, building shared interaction models to unify fragmented products, and collaborating tightly with engineers using component-system thinking (HTML/CSS/TypeScript) to ship resilient UIs that handle partial failures.”

“Machine learning lab assistant at Eastern Illinois University who productionized a voice-enabled conversational AI system: redesigned it with RAG, LoRA fine-tuning (including text-to-SQL), and safety guardrails, then deployed a scalable API supporting ~1,000 daily queries. Also partnered with customer-facing teams during a BlueFi internship by building demos/APIs and accelerating releases via Terraform + AWS CI/CD automation.”

“Backend/data engineer focused on climate/emissions data platforms, building production Python (FastAPI) microservices and AWS serverless/ETL pipelines (Glue/Athena/Lambda/EventBridge). Demonstrated strong reliability and observability practices plus measurable optimization wins, including cutting PostgreSQL query runtimes from minutes to seconds and reducing AWS costs from ~$6k/month to ~$400/month.”

“Built and deployed a production RAG-based internal document Q&A system using LangChain, vector search, and a dockerized FastAPI LLM service. Focused on reliability by systematically reducing hallucinations and improving retrieval through prompt grounding/abstention strategies, chunking and top-k tuning, and iterative evaluation with logged metrics and manual validation.”

“ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.”

“Full-stack developer who owned and shipped an end-to-end web application for LeafNBeyond (React/Node/Postgres), deployed to production at leafnbeyond.com, with reported 35% sales growth and strong UX feedback. Also built Azure-based ETL pipelines using lakehouse/medallion architecture with validation and retry logic, and has AWS fundamentals from a master’s coursework (EC2, RDS, IAM, load balancing).”

Puspa Oli

Screened

Junior Machine Learning Engineer specializing in NLP, Computer Vision, and FinTech AI

Kathmandu, Nepal2y exp

“AI/LLM engineer who has shipped production RAG and agentic systems end-to-end (LangChain/FAISS, OpenAI+Gemini, FastAPI, Docker, Streamlit), focusing on retrieval quality and low-latency performance. Also partnered with a non-technical PM at deepNow to deliver a forecasting + summarization pipeline for daily market insights with iterative prototyping and a simple UI.”

Dhwani Patel

Screened

Mid-level Full-Stack Python Developer specializing in AI/ML and backend APIs

BC, Canada15y exp

“Python/Django backend engineer with open-source experience upgrading Archivematica to Django 4.2 LTS, including resolving a tricky breaking change in datetime parsing by implementing a preservation-safe legacy timestamp conversion layer. Also built a cost-efficient, reproducible Small Language Model (Microsoft Phi-3) fine-tuning pipeline that turns CSV product data into a domain-specific searchable Q&A chatbot, with emphasis on memory optimization and overfitting prevention.”

Venkata Bayarapuneni

Screened

Junior Full-Stack Developer specializing in JavaScript, Python, and cloud-deployed web apps

Remote, USA1y exp

“Built and deployed a production LLM-powered travel assistant (Globe Trote) that automates end-to-end trip planning using a multi-step agent pipeline with RAG, external API calls, and enforced structured JSON outputs. Focused on reliability (validation, retries, fallback prompts, logging) and reported a ~30–40% reduction in irrelevant/generic responses after adding retrieval grounding.”