Vetted Semantic Search Professionals

Gomathy Selvamuthiah

Screened

Junior Data/AI Engineer specializing in MLOps, real-time pipelines, and LLM applications

Portland, US2y exp

“Built an LLM-driven MLOps agent at SBD Technologies that automated an EV-charging prediction workflow end-to-end, integrating with real-time Kafka/FastAPI systems supporting 120K+ chargers at 99.99% event delivery. Addressed frequent schema drift by implementing SQLAlchemy/Flyway validation (60% reduction in drift issues) and deployed as Kubernetes microservices with GitHub Actions CI/CD; also has Airflow-based ingestion/crawling experience into Snowflake and stakeholder-facing delivery via a Fleetcharge PWA.”

Merub SHAIKH

Screened

Junior Software Engineer specializing in full-stack web development and test automation

Chicago, IL3y exp

“Full-stack engineer who built and owned a production workflow/kanban-style drag-and-drop system in Next.js (App Router) with Postgres/Prisma, including reusable component abstractions, Cypress E2E coverage, and post-launch performance/bug ownership. Notable for measurable impact (25% faster UI dev, ~30% query perf improvement) and for leading an incremental Express→NestJS migration that reduced technical debt (~40%) through better structure, docs, and team enablement.”

Mayank VYAS

Screened

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

Tempe, AZ4y exp

“Built production LLM-driven products including a job-hunt AI (job ranking + resume optimization) and an InterviewAI agentic pipeline using LangChain. Focused on practical deployment concerns like securing OpenAI usage via rate limiting and tiered quotas, and demonstrates an applied approach to choosing models, retrieval methods (RAG), and prompting strategies.”

Srikar Tharala

Screened

Mid-level AI/ML Engineer specializing in Generative AI and RAG systems

Remote, USA4y exp

“Currently at ProShare and reports building an AI/LLM-powered system deployed to production, aimed at helping with status-related difficulties and reducing misunderstandings across transactions. Also cites prior collaboration at Porsche with marketing teams, focusing on translating marketing goals into technical requirements and communicating solutions clearly to non-technical stakeholders.”

“Backend-focused engineer with 7+ years of experience building cloud-native, distributed SaaS systems in startup-like environments, including healthcare and enterprise automation platforms. Strong in Go/Python microservices, event-driven architectures, and production AI/LLM integration, with hands-on experience scaling Kubernetes-based systems on AWS while balancing speed, reliability, and evolving product requirements.”

Sampath Achalla

Screened

Mid-level Full-Stack Engineer specializing in Golang and cloud-native FinTech systems

USA3y exp

“Backend-leaning full-stack engineer in fintech/payments who shipped an end-to-end Stripe payments + webhook system for a financial microservices platform, emphasizing ledger accuracy via idempotency, transactional writes, retries, and DLQs. Also delivered a real-time React/TypeScript payment status dashboard informed by user interviews, and improved production performance by 35% p95 latency through PostgreSQL tuning and Redis caching on AWS.”

“Sales professional at AutoSherpa who partners closely with product/engineering to resolve CRM data-sync issues affecting pipeline accuracy and sensitive customer data handling. Experienced running multiple customer onboardings in parallel using structured tracking (CRM/project boards), early risk logging, and weekly stakeholder syncs; targeting $100k base and open to equity.”

“Built an end-to-end agentic population health strategy copilot for healthcare leadership, turning broad chronic disease questions into structured, evidence-backed strategy briefs. Stands out for combining healthcare domain knowledge with production-grade GenAI implementation, including LangGraph orchestration, Databricks/MLflow deployment, human review, and quality gates focused on citations, metrics, risks, and safety.”

“Backend/AI engineer who built an enterprise RAG chatbot over 40,000+ technical documents, owning the system from ingestion and retrieval design through launch, optimization, and incident prevention. Stands out for treating LLM reliability as a data, retrieval, and observability problem—delivering 90%+ benchmark accuracy, ~50% fewer hallucinations, and major gains in lookup speed and latency.”

Narayan Anantha Krishnan

Screened

Junior AI/ML Engineer specializing in LLMs, RAG, and cybersecurity

Syracuse, NY2y exp

“AI/full-stack builder with hands-on experience shipping conversational and agentic products, including a travel itinerary assistant, a multi-agent data analysis platform, and a self-correcting RAG system. Also brings academic research depth from Syracuse University, where they helped develop tiny-LLM-based IoT threat mitigation and presented an accepted paper at FLAIRS 39.”

“Built and owned an internal AI-powered knowledge assistant that centralized fragmented company knowledge across docs, tickets, and internal systems. They designed the backend, ingestion pipelines, vector search, RAG workflow, and APIs, then drove adoption through pilot testing and quality improvements—ultimately automating roughly 30% of support inquiries and cutting resolution time by about two hours per ticket.”

“Built an AI Image Editor end to end with a React frontend and Flask/PyTorch backend, focusing on the hard operational problems of deploying generative AI reliably across CUDA, Apple MPS, and CPU environments. Particularly strong in backend systems, API design, model lifecycle management, and architectural refactoring that improves reliability and future development speed.”

“Built a production multi-agent RAG assistant using LangChain/LangGraph with OpenAI embeddings and FAISS, focusing on retrieval quality and latency (Redis caching, parallel retrieval, precomputed embeddings). Experienced orchestrating ETL/ML pipelines with Airflow and Databricks Workflows, and has delivered an AI assistant for business ops to extract insights from policy/compliance documents through close non-technical stakeholder collaboration.”

“Built and owned a customer-facing Document Intelligence Service for legal contract analytics at Noasis Digital, delivering extraction/summarization with careful accuracy controls (confidence thresholds, versioned deployments, production logging). Also developed a React/TypeScript document review app and internal QA dashboard, and has hands-on microservices experience with async messaging (RabbitMQ), timeout tuning, and centralized structured logging for reliability at scale.”

“Built and deployed a production AI-powered personalized learning platform (Django + FastAPI) featuring an LLM+RAG tutoring assistant and automated grading. Demonstrates strong applied LLM reliability engineering (structured JSON outputs with Pydantic validation, hallucination control via FAISS-based RAG thresholds and refusals) plus scalable async microservice design and Airflow-orchestrated ETL across AWS/GCP.”

Ahmad Alomari

Screened

Senior Data Scientist & Machine Learning Engineer specializing in computer vision and production ML

Cleveland, OH7y exp

“PhD in computer engineering who has built production-oriented ML/NLP systems for space-weather prediction using Spark-based ETL on noisy satellite sensor logs. Strong in entity resolution and semantic search—fine-tuned E5 embeddings with contrastive learning and deployed to Pinecone, improving top-5 retrieval precision by 25%—and emphasizes scalable, observable pipelines with Airflow, Docker, and CI/CD.”

Ramya Jonnala

Screened

Principal/GenAI Engineer specializing in LLMs, RAG, and MLOps

Plano, TX9y exp

“Built a production AI-powered university marking system that automates question generation and grading from PDF course materials using a RAG pipeline (S3 + Pinecone) orchestrated with LangChain/LangGraph and deployed on AWS ECS via Docker/ECR and GitHub Actions CI/CD. Addressed a key real-world LLM challenge—grading consistency—by implementing rubric-based scoring, retrieval re-ranking, and standardized context summarization, validated against human instructors.”

Tharun Chowdary Malepati

Screened

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

5y exp

“AI/LLM engineer who built and deployed a production healthcare RAG chatbot ("DoctorBot") with strict medical safety guardrails, an 85% confidence-gated verification layer, and latency optimizations that cut responses from ~8s to ~2–3s. Also worked on finflow.ai to generate finance/banking test cases from BRDs, collaborating closely with non-technical domain stakeholders, and has hands-on orchestration experience with LangChain/LangGraph and agentic evaluation/monitoring practices.”

“Built and shipped production LLM agents for real-time, high-concurrency conversational systems, including a RAG-based pipeline with dynamic multi-provider routing and failover that achieved 99.99% reliability and sub-800ms latency. Also architected a UAV telemetry chatbot with tool-calling (anomaly detection/summarization), strict schema validation, and robust eval/monitoring loops, cutting tool-call errors by 30% and reducing operational costs by 90%.”

Asad Abdul

Screened

Mid-level Full-Stack Developer specializing in AI-driven cloud-native applications

Laguna Niguel, CA4y exp

“Full-stack engineer with healthcare/ops analytics experience at PatientXpress, shipping a real-time operational dashboard end-to-end (React/TypeScript + Node/Postgres on AWS) that cut manual reporting by 50%. Strong in performance and reliability work—pagination/caching, Postgres indexing/partitioning, Terraform-based AWS provisioning, CI/CD with GitHub Actions, and production incident response with improved monitoring (CloudWatch/Prometheus).”

“AI/ML engineer with hands-on experience owning production deployments from discovery through post-launch stabilization, including real-time computer vision/OCR systems and LLM-powered RAG workflows. Stands out for translating messy customer workflows into reliable backend services, debugging non-deterministic retrieval issues, and hardening AI systems with validation, monitoring, and human-review fallbacks.”

“Full-stack and AI product engineer with strong accessibility and voice-interface experience in senior living, building systems for older adults where trust and usability are critical. Has shipped React/TypeScript and .NET MAUI products, productionized Azure OpenAI features, built RAG-based research tools, and improved both product outcomes and technical performance with measurable impact.”

“Full-stack TypeScript engineer who has built AI-powered product workflows in Next.js/Node.js and owned production backend architecture for a PostgreSQL marketplace app. Stands out for combining product delivery with strong data security patterns and measurable performance gains, including reducing p95 API latency from 780ms to 460ms.”

“Frontend-focused product builder who designed and implemented an internal PCB quoting and pricing platform at SVTronics end to end. They translated a vague operational problem into a shipped React/TypeScript workflow that cut quote preparation time by about 40% and reduced manual errors, showing strength in both UX simplification and production delivery.”