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Vetted Keras Professionals
Pre-screened and vetted.
Junior Robotics & AI Engineer specializing in SLAM, motion planning, and sim2real learning
Mid-level AI/ML Engineer specializing in LLMs, NLP, and scalable ML pipelines
Mid-level Data Scientist specializing in ML, NLP, and production AI workflows
Mid-level Data Scientist specializing in ML, deep learning, and manufacturing analytics
Mid-Level Full-Stack Software Engineer specializing in cloud-native and AI-driven applications
Mid-level Generative AI Engineer specializing in LLMs, RAG, and agentic AI
Staff-level AI/ML Engineer specializing in enterprise RAG, agentic automation, and AI governance
Mid-Level Software Engineer specializing in full-stack, data engineering, and ML
Senior Data Scientist specializing in ML, fraud risk, and Generative AI (RAG/LLMs)
Mid-level AI/ML Engineer specializing in GenAI, computer vision, and real-time ML pipelines
Senior Full-Stack AI/ML Engineer specializing in personalization, NLP, and GenAI platforms
Mid-level Data Scientist / ML Engineer specializing in LLMs and predictive analytics
Senior Data Scientist specializing in healthcare analytics and scalable ML pipelines
KrishnaVarshith Pabbisetty
Mid-level Backend Software Engineer specializing in Spring Boot, microservices, and cloud-native AI
“Backend engineer with experience modernizing a large-scale procurement platform at Jio Platforms by breaking a monolith serving 35 business units into Spring Boot microservices, improving uptime and cutting report latency ~30%. Also built high-concurrency FastAPI systems (200ms at ~500 concurrent users) with strong security (JWT/OAuth2/RBAC), event-driven Kafka integrations, and reliability patterns like exactly-once delivery for ~1M monthly triggers.”
Senior Machine Learning Engineer specializing in MLOps and Generative AI
Senior Software Engineer specializing in robotics, ML, and full-stack web development
Mid-level AI/ML Software Engineer specializing in Generative AI and NLP
Naga Gayatri Bandaru
Mid-level AI/ML Engineer specializing in MLOps and production ML systems
“Backend/ML engineer who has shipped high-scale real-time systems across e-commerce and healthcare: built a PharmEasy real-time recommendation engine for ~2M monthly users (cut feature latency 5 min→30 sec; +15% cross-sell) and architected a HIPAA-compliant multimodal clinical diagnostic workflow (DICOM+EHR) with XAI, MLOps (MLflow/Airflow/K8s), and drift/monitoring guardrails supporting 10k+ daily predictions.”
Abnik Ahilasamy
Intern LLM/GenAI Engineer specializing in RAG, agentic systems, and low-latency inference
“Interned at Larsen & Toubro where they built and deployed an agentic RAG document question-answering system to reduce time spent searching documents and improve trustworthiness. Implemented ReAct-style multi-step orchestration with LangChain/LlamaIndex plus evidence-bounded generation, grounding/citations, and rigorous evaluation—cutting latency ~40%, hallucinations ~35%, and unsafe outputs ~40% while collaborating closely with non-technical business/ops stakeholders.”
Soham Patel
Mid-level Machine Learning Engineer specializing in healthcare NLP and MLOps
“ML/AI practitioner in healthcare (Syneos Health) who has deployed production clinical NLP and risk models. Built a BERT-based physician-note information extraction system on Docker + AWS SageMaker (reported ~42% retrieval improvement) and automated retraining/deployment with Airflow and drift detection, while partnering closely with clinicians to drive adoption (reported ~18% readmission reduction).”
Aakash Mahesha
Junior AI/ML Engineer specializing in anomaly detection and LLM/RAG systems
“Built and productionized a tool-first, multi-agent framework that augments an anomaly detection model with domain context to generate trustworthy, evidence-backed anomaly explanations (including false-positive likelihood). Architected the platform to be model/orchestration/vectorDB agnostic (e.g., GPT + CrewAI + ChromaDB vs Claude + LangGraph + other vector DB) with strong performance, reliability, and OpenTelemetry-based observability. Also built a personal LangGraph-based "mock interviewer" agent that asynchronously fuses voice + live code input using state reducers, stop conditions, and fallback routing.”