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Vetted TensorFlow Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in Generative AI, LLMs, and scalable inference
Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable MLOps
Mid-level Full-Stack Software Engineer specializing in FinTech analytics and security
Senior Software Engineer specializing in cloud architecture and machine learning
Junior AI/ML Software Engineer specializing in NLP, LLM evaluation, and recommendation systems
Mid-level AI/ML Engineer specializing in generative AI, LLMs, and MLOps
Mid-level Software Engineer specializing in backend APIs, data pipelines, and cloud microservices
Executive Robotics & AI Founder specializing in Embodied AI and Robotics Data Infrastructure
Mid-level Applied AI Engineer specializing in LLMs, MLOps, and real-time AI systems
Mid-level AI/ML Engineer specializing in LLMs, multilingual NLP, and low-latency MLOps
Senior AI/ML & Data Scientist specializing in NLP, knowledge graphs, and semantic search
Senior AI/ML Engineer specializing in LLM agents, RAG, and production ML systems
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level Machine Learning Engineer specializing in Generative AI and LLM applications
Senior Software Engineer specializing in AI for Healthcare and Enterprise SaaS
Jared Alessandroni
Executive CTO specializing in AI, cloud platforms, and scaling SaaS products
“NYC-based startup founder/CTO who sold products to Omnicom and Sprinklr, then built an AI-powered cultural insights engine inside Omnicom using AWS Lambda + ML to process ~1M items/day and reached ~$1MM ARR in year one. Former senior leader at Sprinklr managing 200+ people globally, delivering enterprise martech solutions with SLAs and high-reliability social data pipelines (Twitter firehose).”
Abhijay Sai Paladugu
Junior Data Scientist specializing in LLM agents, RAG, and reinforcement learning
“McKinsey practitioner who built and deployed production LLM systems for consultants/clients, including a Power BI-integrated multi-agent chatbot (RAG + text-to-SQL + formatting) with custom Python orchestration, verification loops, and a 100+ case eval set achieving ~95% consistency. Also delivered a taxonomy-mapper agent that standardized inconsistent labeling for C-suite stakeholders, cutting a process from >2 weeks to <30 minutes through demos and business-focused communication.”
Priyanka Kaswan
Senior AI Research Engineer specializing in LLM agents and large-scale ML
“AT&T Labs builder who deployed a production multi-agent LLM system that lets engineers ask natural-language questions and automatically generates deterministic, schema-grounded Snowflake SQL (200–400 lines) to detect anomalies in massive wireless/network event data (~11B events/day). Experienced with LangChain and Palantir Foundry orchestration, RAG-based result interpretation, and rigorous evaluation/monitoring loops to continuously improve reliability.”
Sai Chandra Bandi
Mid-level AI/ML Engineer specializing in Generative AI, LLM alignment, and RAG
“Built and productionized a real-time enterprise RAG pipeline to improve factual accuracy and reduce LLM hallucinations by grounding responses in constantly changing internal knowledge bases (policies, manuals, FAQs). Experienced in orchestrating end-to-end ML workflows (Airflow/Kubernetes), handling messy multi-format data with schema enforcement (Pydantic/Hydra), and maintaining freshness via streaming incremental embeddings plus batch refresh. Also delivers applied ML solutions with non-technical teams (marketing/CRM) for segmentation and personalized engagement.”
Prasanth Peddireddy
Entry-level Supply Chain & Test Engineer specializing in warehouse automation and robotics
“P&G operator who is also building and selling an AI receptionist (voice agent) SaaS for healthcare/service clinics, using EHR + calendar API compatibility to target accounts and letting the Voice AI run parts of the demo to prove value. Has already closed and deployed to two clients in the last two months, with production impact via reduced front-desk overhead and automated scheduling/FAQs, and brings a structured, scalable deployment/process mindset from global WMS rollouts.”