Vetted Recurrent Neural Networks (RNN) Professionals

“Computer-vision engineer with hands-on experience designing UAV-based production imaging systems for object detection/tracking, including camera selection and resolution/zoom tradeoffs. Improved segmentation/measurement accuracy by implementing orthorectification using ground points plus intrinsic/extrinsic calibration to correct perspective distortion, and has built Python/OpenCV pipelines (including barcode-focused grayscale processing and multithreaded execution).”

Isha Harne

Screened

Intern Software Engineer specializing in ML applications and LLM platform engineering

New York, NY1y exp

“Full-stack engineer who builds and scales customer-facing and internal AI products end-to-end (React/TypeScript/FastAPI/MongoDB) with strong product instrumentation and rapid MVP iteration. Built an AI-powered code review assistant adopted across teams and integrated into CI/CD, reducing manual review time by 30%+, and has hands-on experience with LLM retrieval/reasoning systems (LangChain + FAISS) and microservices scaling using RabbitMQ, Docker, and AWS.”

“Healthcare-focused ML/AI engineer who has built clinical note summarization and medical image annotation solutions using LLMs, RAG, and multimodal models. They combine experimentation across major model providers with practical production concerns like monitoring, drift detection, and latency/cost tradeoffs, and also earned 2nd place in a Google hackathon for a medical AI assistant.”

“Backend/platform engineer with strong ownership of production systems, including a full Azure migration from a VM-based monolith to a containerized, event-driven microservices architecture. They combine cloud infrastructure, LLM/RAG optimization, and pragmatic stakeholder management, with measurable wins including 90% infra cost reduction, faster deployments, and significantly improved latency and token efficiency.”

Jaykumar Kotiya

Screened

Mid-level Machine Learning & AI Engineer specializing in Generative AI, NLP, and MLOps

Boston, MA6y exp

“Built and deployed production LLM systems for summarizing sensitive legal and financial documents, emphasizing GDPR-aligned privacy controls and scalable hybrid cloud architecture. Experienced with Kubernetes/Airflow orchestration and rigorous testing/monitoring practices, and has delivered measurable business impact (18% conversion lift) by translating AI outputs for non-technical marketing stakeholders.”

“Backend/data engineer with hands-on production experience across Python/Flask microservices and AWS serverless/data platforms (Lambda, DynamoDB, S3, Glue/PySpark). Demonstrated strong reliability and operations mindset (JWT/RBAC, retries/timeouts/circuit breakers, CloudWatch/SNS alerting) and measurable performance wins (SQL report runtime cut from 10 minutes to 30 seconds). Seeking ~$150k base and cannot travel for onsite meetings for the next 5–6 months due to family medical constraints.”

“Built and productionized an agentic RAG assistant using Ollama + LangChain + MCP + ChromaDB to speed up and standardize access to operational knowledge from tickets and runbooks. Focused on real-world reliability: mitigated timeouts/latency with retries and concurrency limits, improved retrieval via chunking/embedding iteration, and reduced hallucinations through citation-grounding and confidence-based abstention. Also partnered with non-technical ops staff to deliver anomaly detection/monitoring by translating operational needs into model signals, thresholds, and alerting logic.”

Purva Chakravarti

Screened

Senior Full-Stack Software Engineer specializing in scalable web apps, cloud, and blockchain/AI

Chino, California11y exp

“Full-stack engineer with strong production ownership across React/TypeScript, Node.js, and AWS (EC2/ECS/RDS/CloudWatch), including CI/CD, observability, and incident response. Delivered a secure RBAC workflow module end-to-end and achieved measurable gains (~30–40% latency reduction, ~50% error reduction) that lowered infra/ops costs. Comfortable in high-ambiguity startup environments—shipped a payment module within 2 days of joining with no documentation.”

“Healthcare-focused ML/LLM engineer who built a production hybrid RAG workflow to automate prior authorization by retrieving from medical guidelines/historical cases (FAISS) and generating grounded rationales for clinicians. Strong in operationalizing ML with Airflow/Kubeflow/MLflow on SageMaker, optimizing latency (ONNX/quantization/async), and reducing hallucinations via evidence-only prompting; also partnered closely with clinical ops to deploy a readmission prediction tool used in daily rounds.”

Arjun Shrestha

Screened

Intern AI/GenAI Engineer specializing in NLP, RAG, and Snowflake Cortex

Columbus, Ohio1y exp

“Built and deployed a production AI invention/patent review platform that compares invention submissions against patent rules to provide instant feedback, reportedly cutting legal team review time by ~80%. Learned Snowflake Cortex LLMs and production deployment (Docker + AWS) on the job, and validated system quality through human-in-the-loop testing with experienced legal stakeholders.”

Satish Kumar Reddy

Screened

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

Remote, NJ5y exp

“Built and deployed a production LLM/RAG intelligent document understanding platform for healthcare clinical documents (notes, discharge summaries, diagnostic reports), integrating spaCy entity extraction, Pinecone vector search, and a Spring Boot API on AWS with monitoring and guardrails. Demonstrates strong MLOps/orchestration (LangChain, Airflow, Kubeflow/Kubernetes) and a metrics-driven evaluation approach, and partnered with a healthcare operations manager to cut manual review time by 80%.”

Mohammed Syed

Screened

Mid-level AI Engineer & Researcher specializing in healthcare AI and multimodal LLM systems

Remote2y exp

“Backend/ML engineer focused on clinical AI transparency who built ShifaMind, an explainability-enforced clinical ML system using UMLS/MIMIC-IV/PubMed data with RAG, GraphSAGE, and cross-attention. Demonstrated strong production engineering via FastAPI API design and safe migrations (feature flags/shadow inference), plus HIPAA-aligned auth/RLS patterns; also delivered a real-time comet detection system reaching 97.7% accuracy.”