Pre-screened and vetted.
Mid-Level Software Developer specializing in AI/ML and cloud-native microservices
Mid-level Full-Stack Software Engineer specializing in Generative AI and cloud-native systems
Junior AI/ML Engineer specializing in LLM applications, RAG, and multimodal computer vision
Mid-level Generative AI Engineer specializing in RAG systems and AI-powered education tools
Senior Full-Stack & AI Engineer specializing in FinTech and Healthcare
Senior Data Engineer specializing in Machine Learning and Healthcare Data Platforms
Intern Full-Stack Software Engineer specializing in cloud, microservices, and ML/NLP
Mid-Level Software Engineer specializing in cloud microservices and AI automation
Junior Software Engineer specializing in full-stack, cloud, and AI/ML integration
Junior Software Engineer specializing in ML inference infrastructure
Junior Full-Stack Software Engineer specializing in backend, cloud, and AI systems
Senior VR/AR Software Engineer specializing in graphics, OpenXR, and computer vision
Senior Unity Developer specializing in AR/VR and simulation
“Unity developer with VR training simulator experience who improved engagement by fixing a core laser-tracking interaction issue (stability/jitter/response time) based on user and instructor feedback. Has implemented real-time multiplayer features in Unity using Photon Fusion (sync, spawning, replication) and addressed latency/desync with authority management plus prediction/interpolation, while emphasizing scalable practices for large codebases and rapid cross-functional iteration.”
Entry-level Machine Learning Engineer specializing in computer vision and systems
“ML-focused builder who has shipped an end-to-end income-class prediction product: built the data pipeline, trained models, deployed via Streamlit with a live UI, and tracked success via accuracy (84%), adoption, and latency. Demonstrates strong practical MLOps instincts (Docker/Streamlit Cloud, logging/monitoring, caching) and data engineering reliability patterns (schema checks, idempotency, retries, backfills) while iterating quickly in ambiguous, solo-project environments.”
Entry Data Analyst specializing in ETL pipelines and business intelligence
“Analytics candidate with hands-on experience building reliable healthcare reporting layers from messy transactional data using SQL and Python. Stands out for combining data transformation, KPI definition, validation rigor, and performance tuning to deliver reusable reporting assets that improve trust in operational metrics.”
Junior AI Engineer specializing in MLOps, LLMs, and multi-agent systems
“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.”
Junior AI/Software Engineer specializing in NLP, RAG, and resume parsing
“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).”
Intern Robotics & Automation Engineer specializing in ML, IoT, and Computer Vision
“Robotics engineer who built a real, mostly self-assembled autonomous robot (WRAITH) as a final-year project, implementing ROS2-based 2D SLAM (Cartographer/SLAM Toolbox) and Nav2 on a Raspberry Pi 5 under tight CPU/RAM and OS compatibility constraints. Also delivered a full Flutter mobile control app backed by a Flask REST API (manual control, live camera streaming, mapping/navigation) and introduced an image-based verification method to improve localization.”
Mid-level AI Engineer specializing in LLM agents, RAG, and data pipelines
“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.”