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Vetted Data Ingestion Professionals
Pre-screened and vetted.
Prabuddha Banerjee
Mid-level AI Engineer specializing in LLMs, speech AI, and agentic workflows
“AI/backend engineer who has built multiple applied AI systems end-to-end, including an underwriting document intelligence copilot, ambient clinical documentation workflows, and a financial analysis agent. Stands out for combining practical LLM architecture choices with reliability mechanisms like human-in-the-loop review, eval frameworks, and grounded retrieval in production settings.”
Godwin Idowu
Intern Software Engineer specializing in AI/ML and full-stack systems
“Full-stack engineer with hands-on ownership of both real-time marketplace features and production AI systems. Built AI-powered matching at an early-stage startup and shipped LLM-driven CRM and competitive intelligence workflows using OpenAI, LangChain, and RAG, with measurable gains in engagement, workflow reduction, and decision speed.”
Sharanya Guduri
Mid-level Full-Stack Python Developer specializing in Healthcare IT
“Backend/AI engineer with Johnson & Johnson experience building data-heavy payer/claims analytics services (Python/FastAPI, PostgreSQL, AWS) and optimizing them under peak ingestion load via indexing/query tuning and caching. Also shipped an end-to-end RAG feature for clinicians to extract insights from unstructured clinical notes, using constrained prompts and retrieval-confidence guardrails to prevent hallucinations.”
KHUSHBU KAKDIYA
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and cloud MLOps
“Built and deployed a production LLM/RAG system at CVS to automate clinical documents, addressing PHI compliance, retrieval accuracy, and latency; achieved a 35–40% reduction in review effort through chunking and FP16/INT8 optimization. Also has experience translating AI outputs into actionable insights for non-technical stakeholders (sports analysts).”
Harsh Modi
Junior Full-Stack Software Engineer specializing in Node.js microservices and React
“Backend engineer who has shipped both high-throughput real-time systems and production LLM/RAG features. Built a database-free, local-first messaging service (Node/Express/Socket.IO) achieving ~1,500 msgs/sec at <25ms p95, and implemented a Go-based RAG recommendation pipeline with strict JSON/schema validation, catalog grounding, fallbacks, and eval loops that cut hallucinations to ~1–2% while reducing LLM costs ~60%.”
Swati Swati
Senior Data Scientist/Software Engineer specializing in ML systems and cloud DevOps
“AI software engineer with experience spanning LLM/RAG production systems and regulated fintech infrastructure. Built an end-to-end natural-language-to-SQL analytics assistant (Weaviate + GPT-4 + Supabase) shipped as an API with 92% accuracy and major time savings for non-technical users, and also owned demand-forecasting and CI/CD/containerization improvements for a Bank of America core banking deployment at Infosys.”
Visaj Kapadia
Mid-Level Full-Stack Developer specializing in AWS and scalable web platforms
“Software engineer with hands-on AWS experience optimizing an email campaign delivery system—re-architected a monolithic worker into multi-threaded/multi-worker ECS components to boost throughput ~600% (5 to 35 emails/sec). Comfortable debugging production issues (e.g., SQS/EventBridge policy misconfiguration) and emphasizes maintainable delivery via design docs, TDD, versioned APIs, and strong test coverage.”
PremKumar Gandla
Mid-level AI/ML Engineer specializing in MLOps, NLP, and scalable model deployment
“Built and deployed a production autonomous AI data analyst agent (LangChain + GPT + Streamlit on AWS) that turns natural-language questions into validated SQL, visualizations, and insights, cutting manual analysis time by ~50%. Emphasizes reliability and MLOps: schema-aware validation/guardrails to prevent hallucinations, scalable large-data processing, and Azure DevOps CI/CD + MLflow for automated deployment and experiment tracking.”
Srichandan Kota
Senior Full-Stack AI Engineer specializing in Generative AI and FinTech
“Backend engineer who built and owned an AI-powered financial research product end-to-end, using a typed NestJS/GraphQL backend with LangGraph-style agent routing to produce sourced, structured financial analysis. Emphasizes finance-grade correctness (Zod validation, metric registries, unit/empty-result guardrails) while keeping latency low via batching, caching, and fast token streaming, and has led incremental migrations using strangler/feature-flag/shadow traffic patterns.”
Abhinav Garg
Senior Robotics Software Engineer specializing in ROS 2 autonomy and distributed systems
“Robotics Software Engineer with 2.5 years at the Army Research Lab building production tools and cloud infrastructure for large-scale ROS/Unity simulation on AWS. Created a Python GUI to streamline analysis of massive (100GB) ROS bag/MCAP datasets and has deep ROS2/Nav2 performance debugging experience (executor/QoS/TF tracing). Also built an in-house ROS perception pipeline for an assembly-line use case, reaching 92% accuracy.”
Alicia Geng
Entry-level AI/ML Engineer specializing in AWS MLOps and computer vision
“Built and shipped a production RAG question-answering system using LangChain/OpenAI, Docker, and FastAPI, then reduced hallucinations through disciplined retrieval tuning and constrained prompting. Also implemented a custom evaluation framework (QA-pair dataset) to measure faithfulness/relevance and deployed containerized ML microservices on AWS ECS/Fargate with ALB and rolling, zero-downtime updates.”
Raj Patel
Junior Machine Learning Engineer specializing in LLMs and RAG systems
“Production-focused applied ML/LLM engineer who has deployed an LLM-powered RAG assistant and improved reliability through rigorous retrieval evaluation (recall/MRR), reranking, and guardrails that prevent confident wrong answers. Experienced running containerized ML/LLM services on Kubernetes (including AWS-managed layers) with CI/CD and observability, and has delivered a real-time predictive maintenance system using streaming sensor data and time-series anomaly detection in close partnership with maintenance teams.”
Haritha Kuraparthi
Mid-level Full-Stack Developer specializing in cloud data engineering and analytics
“Software developer with hands-on experience owning customer-facing work end-to-end (requirements, implementation, testing, and feedback-driven iteration) using Python and React.js. Also described remodeling an internal legacy page/tool to improve performance and accuracy, and has exposure to microservices and RabbitMQ plus ETL-based system work.”
Sai Krishna Mallikanti
Mid-level AI & Data Scientist specializing in LLMs, RAG, and healthcare NLP
“Built a production LLM/RAG solution for healthcare operations teams to query large policy and care-guideline repositories in natural language. Improved domain alignment using vector retrieval plus parameter-efficient fine-tuning and prompt optimization, validated through internal user testing and metrics, cutting manual lookup time by ~40%. Also has hands-on experience orchestrating automated ML pipelines with Apache Airflow.”
Sumanth Gottipati
Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and FinTech
“At Delta Airlines, built and shipped a production LLM-powered semantic search/troubleshooting assistant over maintenance logs and operational documentation using OpenAI embeddings and a vector database. Implemented hybrid ranking, query enrichment, and structured filters to improve relevance ~35% while optimizing latency via caching and vector tuning. Also designed a scalable Kafka + AWS (Lambda/SQS) ingestion pipeline with strong reliability/observability and an eval loop using real engineer queries and human review.”
Soham Ravindra Lokhande
Junior Software Engineer specializing in agentic automation and AI platforms
“Backend-leaning founding/early engineer who built automation platforms end-to-end: FastAPI/Python services integrated with a Next.js/TypeScript frontend, including a production VNC streaming URL endpoint for cloud-instance desktop viewing. Also designed core Postgres user/workflow data models and built an agentic orchestration system with LangChain/LangGraph (sub-agents, validators, pause/resume), plus made scalability tradeoffs like S3 pre-signed uploads to keep microservices responsive.”
Smruti Singh
Mid-level Implementation Engineer specializing in enterprise integrations and IAM/PAM
“Data/ML engineer with end-to-end ownership of donor-data deployments for a university foundation, delivering major performance and data-quality gains (500K+ records; 24h to 6h processing; duplicates 5% to 1%). Has put an LLM-assisted enrichment workflow into production with retrieval-grounded business rules, versioned outputs for traceability, and strong operational rigor around validation, logging, and CI/CD.”
Hari Billa
Mid-level Data Scientist specializing in machine learning, NLP, and healthcare AI
“Senior data scientist with hands-on ownership of production ML and GenAI systems across enterprise churn, clinical Q&A, and real-time fraud detection. Stands out for combining strong MLOps discipline with measurable business impact, including $2M+ retained revenue, 10K TPS low-latency fraud infrastructure, and a clinician-reviewed RAG system that improved retrieval accuracy by ~38%.”
Ruturaj Dixit
Junior Data Scientist specializing in AI/ML and product analytics
“Applied ML/data scientist who has owned backend-heavy AI systems end-to-end, including a market-signal platform on FastAPI/AWS and rapid MVP delivery in medical computer vision. Particularly interesting for teams needing someone who can combine model development, backend APIs, production debugging, and pragmatic low-latency architecture decisions.”
Ramya Sree Kanijam
Mid-level Software Engineer specializing in backend systems, cloud, and AI pipelines
“Built and owned an end-to-end AI-driven content enrichment pipeline for a news workflow, using n8n, LLM agents, and external APIs to automate ingestion, deduplication, categorization, and approval routing. Stands out for production-minded AI systems work: they improved reliability with schema validation, retries, idempotency, and monitoring, while automating 90% of processing and cutting duplication errors by 95%+.”
Shreyas Ravishankar
Entry-level Blockchain R&D Engineer specializing in zero-knowledge systems and ML
“Backend/full-stack engineer with an unusual mix of web application delivery, zero-knowledge cryptography, and applied ML. They built a BioTrack system that improved reporting by 40%, and also shipped a privacy-preserving but compliance-aware ERC-20 transaction layer using Noir and ZK proofs, processing 1000+ anonymous compliant transactions in just 2 weeks.”
Raj Kale
Junior Software Engineer specializing in full-stack systems and FinTech
“Full-stack engineer with experience building financial and hiring-product systems, spanning React/TypeScript dashboards, Flask/Kafka/Postgres backends, and multi-tenant configuration for 3,000+ clients. Stands out for combining deep technical debugging and performance work with product-minded UX improvements, including a 41% lift in resume matching accuracy and ~40% latency reduction through batching and query tuning.”
Sai Revanth Evani
Mid-level AI/ML Engineer specializing in RAG systems and Python cloud backends
“Frontend engineer with hands-on experience building AI-powered document search and analytics products, including RAG-based knowledge retrieval interfaces with citations, filters, and document previews. Stands out for combining React/TypeScript architecture with production performance tuning using profiling tools, memoization, lazy loading, and debounced data flows to keep complex, document-heavy UIs responsive.”
Aryaa Deshpande
Junior AI Engineer specializing in ML, LLM systems, and RAG
“Built and deployed an LLM/applied-ML system enabling efficient extraction of useful information from large unstructured multimodal datasets, owning the full pipeline from ingestion to inference and APIs with a strong emphasis on production reliability, latency, and monitoring. Also delivered a voice-based AI workflow for Hindi policy document access for the Election Commission of India by translating non-technical usability needs into iterative demos and a successful implementation.”