Vetted AWS Glue Professionals

“Senior technologist (25 years experience) who served as chief architect/CTO for a patented software startup that was acquired. Strong at building scalable, robust, technology-agnostic systems and translating technical value into investor-ready narratives (forecasts, roadmaps, documentation). Currently prefers joining an existing founding team as a key technical leader/mentor rather than leading entrepreneurship solo.”

Bhavya Sri Gunnapaneni

Screened

Mid-level AI/ML Engineer specializing in fraud detection and NLP

United States4y exp

“Built production AI/RAG-style systems for message Q&A and insurance claims workflows, combining data ingestion, indexing/retrieval, and LLM integration with fallback modes. Has hands-on orchestration experience (Airflow, Prefect, LangChain) and cites large operational gains (claims processing reduced to ~45 seconds; manual review -50%; false alerts -30%) through automated, monitored pipelines and close collaboration with non-technical stakeholders.”

Monisha Nettem

Screened

Mid-level AI/ML Engineer specializing in LLMs, RAG, and MLOps

USA5y exp

“AI/ML engineer with banking domain experience (M&T Bank) who built a production credit-risk prediction and reporting platform combining ML models (XGBoost/TensorFlow) with a RAG pipeline (LangChain + GPT-4) over compliance documents. Delivered measurable impact (≈20% better risk detection/precision, 50% less manual reporting) and productionized workflows on Vertex AI/Kubeflow with CI/CD and monitoring; also implemented embedding-based semantic search using FAISS/Pinecone.”

Rambabu Dhanavath

Screened

Mid-level Software Engineer specializing in backend and cloud-native microservices

United States5y exp

“Backend/cloud engineer with 5 years of experience who has shipped a production internal ops LLM assistant end-to-end using Spring Boot microservices on AWS. Stands out for designing controlled, safety-first agent orchestration with deterministic tool access, Redis/DB-backed recoverable state, and strong observability/evaluation practices to improve reliability in production.”

“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.”

“Backend/data engineer with production experience building Python FastAPI services and AWS-native data pipelines. Has delivered containerized and serverless workloads (ECS/EKS/Lambda) with Terraform-based IaC, strong reliability patterns (JWT/RBAC, retries/circuit breakers, observability), and AWS Glue ETL into S3/Redshift. Demonstrated measurable SQL performance wins (40–50s to <4s) and owned real pipeline incidents through detection, mitigation, and prevention.”

“Backend/data engineer who built a production booking platform on FastAPI microservices (Postgres/Redis/gRPC) and delivered AWS infrastructure spanning Lambda, ECS, SQS, and Glue-to-Redshift analytics. Demonstrated measurable SQL optimization (10 minutes to <40 seconds) and strong operational ownership through monitoring, incident response, and schema-evolution hardening.”

“Built a production RAG-based healthcare chatbot to retrieve patient medical documents spread across multiple platforms, reducing manual and error-prone searching. Implemented semantic search with custom embeddings (Hugging Face) and Pinecone, deployed via FastAPI/Docker on AWS SageMaker with MLflow tracking, and optimized fine-tuning cost using LoRA while orchestrating retraining pipelines in Airflow.”

“ML/LLM engineer with production experience building a compliant RAG-based virtual assistant at Intuit, optimizing embeddings and FAISS retrieval (including PCA) for low-latency, privacy-controlled search and deploying via AWS SageMaker containers. Also built scalable Airflow+MLflow pipelines using Docker and KubernetesExecutor, cutting training cycles by 37%, and partnered with civil engineers/project managers at Aegis Infra to deliver predictive maintenance for construction equipment.”

“LLM/GenAI engineer who built a production AI-powered credit risk policy summarization and compliance alerting platform at HCL Tech, focused on factual accuracy and auditability for a financial client. Implemented a multi-retriever LangChain RAG architecture with citations-only prompting, fallback agents, and human-in-the-loop legal review—cutting manual review time by 35% and scaling to 12 teams.”

Vidit Naik

Screened

Junior AI/ML & Full-Stack Engineer specializing in LLMs and RAG systems

San Francisco, CA2y exp

“Forward-deployed engineer who built a production AI drone-control chatbot that lets users fly a drone via natural language while viewing a real-time feed. Implemented RAG over drone SDK documentation (vector DB + top-k retrieval) and LoRA fine-tuning, with a focus on latency, token efficiency, and cost reduction, and regularly works with non-technical clients to integrate and explain AI system architecture.”

Rahul Solleti

Screened

Mid-Level Software Engineer specializing in cloud-native microservices and full-stack web apps

United States5y exp

“Backend/platform engineer focused on real-time financial fraud detection and transaction monitoring, building low-latency FastAPI + Kafka systems with strong reliability patterns (DLQs, idempotency) and cloud observability. Has hands-on Kubernetes delivery across AWS EKS and Azure AKS with automated CI/CD and GitOps-style deployments, plus experience migrating legacy C# / Java monoliths to containerized microservices using Terraform/ARM and zero-downtime rollout strategies.”

Snehitha Penumaka

Screened

Mid-level AI/ML Engineer specializing in predictive modeling and cloud ML pipelines

Dallas, TX3y exp

“LLM engineer/data engineer who has deployed production RAG systems for internal-document Q&A, building end-to-end ingestion, embedding, vector search, and FastAPI serving while actively reducing hallucinations and latency through rigorous retrieval tuning and caching. Also experienced in orchestrating cloud data pipelines (Airflow, AWS Glue, Azure Data Factory) and partnering with non-technical business teams to deliver AI solutions like automated document review.”

Shabari Vignesh

Screened

Mid-level Data Engineer specializing in cloud data platforms and AI agents

Santa Clara, CA6y exp

“Data/Backend engineer who has owned end-to-end merchant analytics systems on AWS: orchestrated multi-source ingestion (FISERV/Shopify/Clover) with Step Functions/Lambda, enforced strong data quality gates, and served curated datasets via Redshift and a FastAPI layer. Also built an early-stage Merchant Insights AI agent that converts natural language questions into SQL using OpenAI models, with full CI/CD and observability.”

“Analytics professional with experience spanning financial services and e-commerce, including NAV reconciliation/reporting at State Street and revenue optimization work on checkout and retention. Stands out for combining strong SQL/Python data engineering with business-facing analytics, delivering measurable gains like 3-5% conversion improvement and 8-10% payment success uplift.”

“Backend-leaning full-stack engineer with strong infrastructure depth who has owned high-scale production systems end to end, including an event ingestion pipeline that reached 200k+ events per second with zero data loss after launch. Also has hands-on AI experience building a Bedrock-based multi-agent travel assistant with RAG, plus cross-stack healthcare work and business-process automation that cut manual effort by 90%.”

“Software engineer with MERN stack experience who says they have built enterprise and SaaS products, set up CI/CD pipelines using GitHub Actions and Jenkins, and created reusable UI components across an application. They also referenced using AI tools like Claude, GPT, Codex, and Cursor with agentic AI concepts, plus CloudWatch-based tracing and crash monitoring.”

“Backend/data engineer focused on high-scale, event-driven AWS ingestion systems (SQS/Lambda/EKS) processing millions of events per day, with strong reliability patterns (idempotency, DLQs, bounded retries) and deep observability using Datadog distributed tracing. Has delivered Terraform/GitHub Actions CI/CD and improved secret rotation via Secrets Manager + IRSA, plus Glue-based ETL with schema-evolution handling and Postgres SQL optimization (including JSONB/GIN indexing). Candidate is currently living outside the US and states they do not have US work authorization.”

“ML/AI engineer who built and productionized an NLP system at PurevisitX, orchestrating end-to-end ML workflows with Airflow (S3 ingestion through auto-retraining) and optimizing for drift and low-latency inference. Also partnered with Citibank risk teams on a fraud detection model, translating results via dashboards and iterating thresholds based on stakeholder feedback.”

“Software engineer with experience at Wipro Technologies and Wells Fargo building React-based SPAs, reusable component libraries, and developer documentation. Demonstrated strong performance engineering (React.memo, list virtualization, code splitting) with reported >50% rendering-time improvement, plus hands-on production support by diagnosing API outages via monitoring/logs and implementing traffic/server fixes. Comfortable leading workstreams in fast-changing environments using Kanban and tight stakeholder feedback loops.”

“Built and shipped a production agentic LLM analytics platform that lets non-SQL business users query relational databases in plain English via a RAG + LangChain/LangGraph workflow and FastAPI service. Emphasizes safety and reliability with guardrails (validation/access control), testing/evaluation frameworks, and performance optimization (caching, monitoring, Dockerized scalable deployment), reducing dependency on data teams and speeding analytics turnaround.”

“Software/ML engineer who built a production automated job-finding and cold-email personalization system for Fortune 500 outreach, using JobSpy for dynamic scraping, LangChain orchestration, and LLM+vector DB semantic search with grounding/relevance metrics and guardrails. Also delivered a predictive investment analytics platform for financial advisors, communicating results via Tableau dashboards and portfolio KPIs like Sharpe ratio and drawdowns.”

“Full-stack engineer with healthcare and fintech experience who has owned production features end-to-end—most notably an AI assistant clinical risk summary tool on AWS (FastAPI/Lambda + React/TypeScript) that cut analyst review time ~40%. Strong in performance tuning for large datasets (S3/Athena), production ops/observability (CloudWatch, CI/CD, env separation), and building reliable ETL/integrations with idempotency and retries.”

“Has worked at a startup within an incubator program and is a certified mentor with a startup mentorship non-profit focused on early-stage/pre-funded companies. Not currently seeking to found a company or take equity-only roles, but open to working at very early-stage companies (pre-launch/pre-revenue/pre-funded).”