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Vetted Embeddings Professionals
Pre-screened and vetted.
Mid-Level Backend Engineer specializing in cloud-native distributed systems and data pipelines
Senior Machine Learning Engineer specializing in LLM systems and generative AI
Senior AI/ML Engineer specializing in conversational AI and ML platforms
Senior Software Engineer specializing in healthcare integrations, microservices, and AI-assisted systems
Junior Software Engineer specializing in AI, distributed systems, and recommendation systems
Senior Software Engineer specializing in AI-powered search and backend systems
Senior AI/ML Engineer specializing in conversational AI and contact center automation
Mid-level Software Engineer specializing in AI platforms and backend systems
Staff Software Engineer specializing in full-stack platforms and cloud-native microservices
Mid-Level Full-Stack Software Engineer specializing in FinTech platforms
Senior Machine Learning Engineer specializing in MLOps and GenAI platforms
Senior AI/ML Engineer specializing in LLM systems and conversational AI
Senior AI/ML Engineer specializing in ML infrastructure and conversational AI
Senior Software Engineer specializing in distributed systems and agentic AI platforms
Principal software engineer and technical founder specializing in AI platforms
Ashutosh Choudhari
Mid-level AI/ML Engineer specializing in LLM applications and cloud-native systems
“LLM engineer who has shipped production AI systems, including an RFP requirements extraction platform (OpenAI o4-mini + Azure AI Search + FastAPI) achieving 90%+ accuracy and ~5x throughput through grounding, structured outputs, parallelization, and caching. Also partnered with legal/compliance stakeholders at Nexteer Automotive to deliver an AI document comparison tool with traceability and confidence indicators, adopted by non-technical users and saving ~2 FTEs of review time.”
Devisri Veeramachaneni
Senior Software Engineer specializing in cloud backend systems and LLM-powered agents
“Amazon Fire TV Devices engineer who built and shipped a production LLM-powered lab triage and validation system that grounds recommendations in internal runbooks/known-issue data and pushes evidence-based actions via dashboards and Slack. Emphasizes safety and measurability with structured JSON outputs, replay-based evaluation on historical incidents, and production metrics (e.g., disagreement rate and time-to-first-action), plus cost/latency optimizations like caching, batching, and rule-based fast paths.”
Rohith Sadanala
Mid-level Machine Learning Engineer specializing in Generative AI and MLOps
“LLM/agent engineer who has shipped production RAG chatbots in sustainability-focused domains, including a packaging recommendation assistant that standardized messy user inputs and used Pinecone-backed retrieval over product/regulatory data. Experienced orchestrating end-to-end ML workflows with Airflow and AWS Step Functions/Lambda, emphasizing reliability (property-based testing, circuit breakers, OpenTelemetry) and measurable performance (latency/cost). Partnered closely with non-technical leadership to ship 3 weeks early, driving adoption by 150+ businesses and ~20% reported waste reduction.”