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Vetted Convolutional Neural Network (CNN) Professionals
Pre-screened and vetted.
Senior AI/ML Engineer specializing in Generative AI, LLMs, and data platforms
Senior AI/ML Engineer specializing in Generative AI, RAG, and multimodal LLM systems
Director-level Applied ML Engineer specializing in GenAI, LLM systems, and MLOps
Mid-Level Software Developer specializing in AI/ML and cloud-native microservices
Mid-Level Software Engineer specializing in ML and Generative AI applications
Mid-level AI/ML Engineer specializing in risk modeling, healthcare analytics, and MLOps
Mid-level Generative AI Engineer specializing in LLMs, RAG, and NLP systems
Junior Software Engineer specializing in ML inference infrastructure
Junior Full-Stack Software Engineer specializing in backend, cloud, and AI systems
Mid-level AI Engineer specializing in agentic LLM workflows and RAG systems
Samuel Braude
Junior Computer Science student specializing in robotics, ML, and quantum computing research
“Hands-on engineer who has taken an LSTM Bitcoin forecasting model from notebook to a production-grade, monitored API (Docker/Gunicorn/Nginx, Prometheus/Grafana, blue-green rollback) delivering 99.9% availability and ~110–120ms p95 latency. Also built an RFID self-checkout prototype spanning Raspberry Pi + firmware + networking, using deep instrumentation to eliminate double-charges/timeouts (<0.1%) and reduce checkout time ~20% through idempotency, debounce logic, and hardware fixes.”
Yasaswini Majety
Intern AI/ML Engineer specializing in LLMs, RAG, NLP, and MLOps
“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.”
Utkarsh Vijayvergia
Entry-level Software Engineer specializing in full-stack, cloud, and AI systems
“Builder with hands-on experience shipping full-stack products across AWS cloud infrastructure, React/TypeScript apps, SQL-backed systems, and privacy-focused AI workflows. Stands out for combining cost-aware architecture, strong debugging instincts, and product thinking—from an e-commerce platform automated with IaC to a university admin portal serving 10,000+ users and a locally run AI assistant with configurable guardrails.”
Sukhad Joshi
Junior Data Scientist specializing in applied ML, LLMs, and analytics automation
“Research Analyst at Syracuse who deployed an LLM-powered lab automation system allowing researchers to run QCoDeS instrument workflows via natural language, with strong safety guardrails for real instruments and multi-instrument support. Also collaborated with non-technical stakeholders at iConsult on an audio classification/recommendation pipeline, translating business goals into metrics and Tableau dashboards with model comparisons and A/B test results.”
“LLM/RAG engineer at Connex AI who built and deployed a production healthcare agent to extract clinical insights from medical data/notes. Strong focus on real-world reliability—hallucination mitigation (citations, schema validation, confidence thresholds, rejection logic), custom LangChain orchestration (query rewriting, fallback paths), and production evaluation/observability—while collaborating closely with clinical SMEs to ensure clinical fit and time savings.”
Karthikeya Arra
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and MLOps
“Backend/ML engineering candidate focused on fintech automation who architected a zero-to-one agentic/LLM-enabled system to reconcile messy financial documents and bank transactions, reporting ~40% operational efficiency gains. Experienced migrating monoliths to event-driven microservices with incremental rollout via reverse proxy, and implementing production-grade security (OAuth2/JWT, RBAC, Supabase RLS) plus resilience patterns (timeouts/retries under concurrency).”
Lalith Sagar Kambala
Junior Full-Stack Software Engineer specializing in AI-powered SaaS
“Worked on an AI-adjacent search/results product with a React front end and an API-driven backend, focusing on scalability and performance. Emphasizes decoupled JSON API architecture, React rendering optimizations (useMemo/useCallback), and large-dataset techniques like virtualization, plus strong user-issue triage via log analysis and edge-case fixes in query handling/ranking.”