Vetted Keras Professionals

“Built and deployed an LLM-powered clinical decision support and risk monitoring platform for mental health at Valuai.io, emphasizing low-latency, evidence-grounded responses and crisis-safe behavior with clinician escalation. Strong production agent-orchestration background (LangChain/CrewAI) plus rigorous evaluation (clinician-in-the-loop + evaluator agent) and large-scale synthetic testing; also applied multi-agent workflows to document verification and fraud detection during an AI internship at Nixacom.”

“IT Analyst/research background with hands-on experience deploying and hardening a multi-agent AI support/triage system (ticket ingestion + knowledge-base retrieval) with strong emphasis on reliability and observability. Has debugged real production issues spanning backend services and network latency (sync failures/partial writes) and is comfortable in Linux environments; also has academic exposure to robotics simulation and ROS2.”

Pranava Reddy Kothapally

Screened ReferencesStrong rec.

Junior Data Engineer specializing in Azure, CRM data pipelines, and marketing personalization

Hyderabad, India2y exp

“LLM/AI engineer who has deployed production RAG conversational analytics and Text-to-SQL systems over Snowflake and curated data marts, emphasizing enterprise-grade guardrails for accuracy, security, and cost. Notable for a structured approach to reducing hallucinations (curated metric/table registry, SQL validation, RBAC, and citation-backed responses) and for building resilient, observable multi-step agent workflows using LangChain/LlamaIndex and Airflow.”

“Built a LangGraph-based, tool-routing LLM chatbot to deliver fast, trustworthy investment-stock insights (including tariff impact) and deployed it to production on Snowflake after initially developing in Azure with AI Search and the Microsoft Agent Framework. Improved routing robustness by moving from LLM-based decisions to a deterministic router backed by schema-relationship graphs and YAML metadata, and ran the project iteratively with non-technical stakeholders over an 8-month engagement.”

“Built a production-grade, semi-automated document recognition and classification system for large volumes of scanned PDFs, starting from little/no labeled data and handling highly variable scan quality. Deployed on AWS using SageMaker + Docker and orchestrated on EKS with a microservices design that scales CPU-heavy OCR separately from GPU inference, with strong reliability controls (validation, fallbacks, retries, readiness probes).”

“Built and deployed an LLM product called "Content Craft" combining BART-based summarization with a RAG Q&A chatbot using LangChain, embeddings, and a vector database. Has hands-on MLOps experience containerizing and serving models with FastAPI and running them on Kubernetes with monitoring, self-healing, and autoscaling, and has practical experience reducing hallucinations through structured prompting.”

“LLM/agentic AI builder who deployed a production ITSM automation agent on Google ADK integrating ServiceNow and FreshService, with strong safety guardrails (human-approval gating and runbook-only command execution) and rigorous evaluation (500 synthetic tickets; 80%+ false-positive reduction). Also partnered with finance to deliver an AI agent that automated invoice/SOW retrieval and monthly reporting to account managers, reducing manual back-and-forth.”

“Independent builder of production-grade systems: shipped an end-to-end URL shortener with JWT auth, Redis rate limiting/caching, Postgres, Docker, and real-time analytics, and separately architected a Redis-backed distributed task queue handling 1000+ tasks/min. Demonstrates strong distributed-systems instincts (atomicity, retries/DLQ, idempotency, heartbeats) plus a focus on maintainable code and self-documenting APIs (FastAPI/OpenAPI, versioned routes).”

“Built GynAI, an end-to-end maternal clinical decision support platform for OB/GYN practices and hospitals in North America, combining predictive ML with RAG-based LLM explainability. The candidate emphasizes real production ownership across experimentation, deployment, monitoring, and iteration, with reported impact including fewer delayed interventions in high-risk pregnancies and a 15-20% reduction in false positives.”

“LLM engineer/data analyst who built a production RAG QA assistant over the Jurafsky & Martin NLP textbook to reduce hallucinations and provide explainable, source-grounded answers. Experienced with LangChain/LangGraph orchestration, retrieval optimization (embeddings, vector DBs, caching), and rigorous evaluation/monitoring (Retrieval@K, A/B tests, telemetry/drift). Previously communicated analytics insights to non-technical stakeholders at GS Analytics using Power BI and simplified reporting.”

“Built and shipped a real-time AI robotic inspection system, using a synthetic data generation pipeline to address rare edge cases—cutting data collection costs ~60% and boosting hard-scenario accuracy ~20%. Experienced in productionizing ML on constrained Jetson hardware and orchestrating end-to-end ML workflows with Airflow/Docker/Kubernetes, with a metrics-driven approach to reliability, evaluation, and stakeholder communication.”

Manas Agarwal

Screened

Junior Full-Stack Software Engineer specializing in Python APIs, React, and cloud AI integrations

Superior, CO2y exp

“Customer-facing software engineer who builds and deploys practical AI/RAG solutions (e.g., an AI assistant for searching billing PDFs) by deeply understanding support workflows and iterating with users. Demonstrates strong production instincts—quickly stabilizing peak-traffic API timeouts with caching/background jobs, then implementing durable fixes with proper monitoring and maintainable code practices.”

Ttanvi Tummapudi

Screened

Junior Data Scientist specializing in machine learning, predictive modeling, and applied AI research

2y exp

“Data scientist/researcher who has built two multimodal LLM systems: an AI-assisted medical triage pipeline using GPT-4o vision + RAG with confidence-scored red/yellow/green outputs, and a master’s project on multimodal cyberthreat detection combining multiple models and using TinyLlama to generate human-readable risk reports. Also partnered with business analysts at Sanvar Technologies to deliver a churn prediction pipeline and Tableau dashboard for decision-making.”

“Built and deployed real-world systems end-to-end across security and healthcare contexts: led a 3-person team delivering a university vehicle tracking system with 30% cost savings and 1-year post-launch monitoring. Also implemented a healthcare RAG chatbot with adaptive query routing that cut LLM costs by 40% while maintaining answer accuracy, and has experience debugging non-deterministic LLM behavior in DevOps pipeline automation.”

“Data analytics and BI professional with experience turning messy institutional and customer data into decision-ready reporting and predictive systems. They combine strong SQL/Python execution with end-to-end ownership of churn analytics, stakeholder alignment, and operational rollout into dashboards and CRM workflows.”

“Backend/cloud engineer from Resilient Tech with hands-on experience deploying REST APIs and database migrations into a live ERP used by real customers while maintaining 99% uptime. Has debugged intermittent AWS container timeouts down to security group/load balancer misconfigurations, and has extended Python in an ERPNext system to meet GST/e-invoicing compliance requirements with strong customer collaboration.”

“LLM/agent systems builder who deployed a production hybrid router for immerso.ai that dynamically selects retrieval vs reasoning vs generative pathways, achieving an 82% factual-accuracy lift. Deep hands-on experience optimizing local Mistral 7B inference (4–5 bit GGUF quantization, KV-cache reuse) and building reliable RAG/agent workflows with LangChain/LangGraph/AutoGen across GCP Cloud Run and AWS (ECS/Lambda).”

“Built and deployed a production generative-AI copilot at Tungsten that automates invoice/form extraction template creation, reducing weeks of manual model-building work. Combines fine-tuned LLMs (PyTorch/HuggingFace) with OpenCV layout grounding to reduce hallucinations, and runs an end-to-end Kubeflow-based MLOps pipeline with drift monitoring, canary releases, and automated retraining.”

Varun Mahankali

Screened

Junior Full-Stack Software Engineer specializing in React, Node.js, AWS, and Generative AI

3y exp

“Built and production-deployed a Streamlit-based PDF RAG chatbot using LangChain (FAISS, embeddings, prompt templates) and OpenAI, optimizing Streamlit’s stateless behavior by caching vector DB + chat history to cut latency and API cost. Demonstrates a rigorous evaluation mindset (gold datasets, unit tests, LLM-as-judge, groundedness KPIs) and has experience communicating privacy/accuracy safeguards (RBAC, data masking, citations) to a non-technical client at Kalven Technologies.”