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Vetted Keras Professionals
Pre-screened and vetted.
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and AI personalization
Mid-level Machine Learning Engineer specializing in NLP, LLMs, and deep learning
Principal Software Engineer specializing in enterprise AI/ML and multi-cloud architecture
Mid-level Data Engineer specializing in cloud data pipelines and analytics platforms
Principal Data Scientist specializing in Generative AI, LLMs, and ML platforms
Junior Software Engineer specializing in AI/ML and FinTech systems
Mid-level AI/ML Engineer specializing in GenAI, RAG, and cloud-native ML platforms
Mid-level AI/ML Engineer specializing in recommender systems, NLP, and MLOps
Mid-Level Full-Stack Software Engineer specializing in FinTech and AI risk scoring
Mid-level AI Data Scientist specializing in financial risk, fraud detection, and NLP/LLM systems
Mid-level Full-Stack Developer specializing in FinTech and fraud detection
Senior Data Scientist specializing in Generative AI, NLP, and MLOps
Senior DevOps Engineer / AWS Solutions Architect specializing in Kubernetes and DevSecOps
Senior AI/ML Engineer specializing in GenAI, LLMs, NLP, and MLOps
Executive Engineering Leader specializing in Telehealth Platforms and Healthcare IT
Alp Komban
Junior Machine Learning Engineer specializing in computer vision for medical imaging
“Applied ML/LLM practitioner working in healthcare-facing products, using RAG and LoRA fine-tuning on medical data and implementing production monitoring (confidence scoring) for clinician oversight. Has hands-on experience debugging agentic/LLM pipelines (including OCR preprocessing fixes) and regularly delivers technical demos to doctors, investors, and conferences—contributing to adoption and even helping close a funding round through end-to-end pipeline walkthroughs.”
Akash Patil
Mid-Level Software Engineer specializing in backend systems and LLM/RAG applications
“Backend/AI engineer at Intuit who built a production AI-powered case assistant for support agents (FastAPI on AWS EKS) combining Postgres case data, OpenSearch retrieval with embedding reranking, and internal LLMs. Improved peak-season reliability by diagnosing P95/P99 timeout spikes and cutting P95 latency from ~800ms to <400ms via composite indexing, keyset pagination, connection pool tuning, and caching, while adding grounded-generation guardrails (evidence packs, confidence thresholds, fallbacks, human-in-the-loop).”
Haider Shah
Principal AI/ML Architect specializing in GenAI, LLMs, RAG, and Agentic AI
“FinTech/AI engineer who has shipped an end-to-end discrepancy-detection product for financial managers using Next.js, FastAPI/GraphQL, Pinecone, and AWS (with dev/staging/prod, observability, A/B testing, and documentation). Also built an AI-native “AI Genesis” system with agentic cyclic workflows, routing, and tool use, and has experience modernizing legacy systems via the strangler fig pattern while coordinating with senior stakeholders on a 5G autonomous simulation platform.”