Vetted Containerization Professionals

“Built a production GenAI-powered analytics assistant to reduce reliance on data analysts by enabling natural-language Q&A over Databricks/Power BI dashboards, backed by vector search (Pinecone/Milvus) and a Neo4j knowledge graph, including multimodal support via OpenAI Vision. Demonstrates strong real-world LLM reliability engineering with strict RAG, LangGraph multi-step verification, and Guardrails/custom validators, plus broad orchestration and production monitoring experience (Airflow, ADF, Step Functions, Kubernetes, Prometheus/CloudWatch).”

“Built and shipped a real-time oncology risk prediction system used by doctors during patient visits, trained on clinical data in AWS SageMaker and deployed via FastAPI with sub-second responses. Emphasizes clinician-trust features (SHAP explainability, validation checks) and HIPAA-compliant controls (encryption, RBAC, audit logging), plus Kubernetes-based production operations with autoscaling, monitoring, and drift/retraining workflows; collaborated closely with oncologists at Flatiron Health.”

“Built a production RAG-based GenAI copilot backend at Aetna using Python/FastAPI, GPT-4, LangChain, and Azure AI Search, deployed on AKS with Prometheus/Grafana observability. Owned the system end-to-end (ingestion through deployment) and improved peak-time reliability by addressing vector search and embedding bottlenecks with Redis caching, index optimization, and async processing, plus added anti-hallucination guardrails via retrieval confidence thresholds.”

“Software engineer at Wrap who built production AWS Lambda services for large-scale Parquet dataset generation (50k+ records) and a synthetic traffic/lead generation system using Python, Playwright, and Jenkins. Also built and deployed a full-stack hobby product (MyAnimeListRanker) that ingests MyAnimeList user data and uses an Elo-based ranking workflow, with operational guardrails like rate limiting and monitoring via Vercel/logs.”

“Insurance-tech CTO and repeat founder with 10+ years in insurance startups; was employee #4/CTO at Polly (formerly DealerPolicy) and helped scale it from a PowerPoint to 250 employees while raising $180M+. Currently building and selling AgentCanvas.ai—an extensible AI accelerator platform for large insurance agencies—after coding the product end-to-end and now running demos/POCs with prospective buyers.”

“Engineering lead/player-coach working on project management software, owning a high-impact scheduling algorithm rewrite focused on performance, maintainability, and documentation after prior team turnover caused regressions. Previously at EOX Vantage, helped an insurance client modernize monolithic workflows by scoping ambiguous requirements into a roadmap and shipping a day-one MVP before iterating with value-add features.”

“JP Morgan engineer and Texas A&M student web developer who has owned production systems end-to-end, including a real-time ML training workflow that improved internal search relevance by 30%. Experienced with AWS cloud migrations and operating containerized services on ECS with CloudWatch+ELK observability, Terraform infra, and Spinnaker CI/CD; also built event-driven pipelines with RabbitMQ and Elasticsearch at 1M+ record scale.”

“Full-stack engineer who built an enterprise search platform (Codlens) delivering natural-language Q&A over Jira/Slack using embeddings, vector DB search, re-ranking (RRF), and LLM responses with source grounding. Also designed and benchmarked a distributed IAM system with Postgres transaction-log replication and Raft-based quorum consistency, reporting ~253 TPS at ~60ms latency in a multi-node setup. Experience spans early-stage startups (Zetic AI, Sagwara Capital) and large-scale orgs (Akamai, Atlassian).”

“Founding engineer at a civic-tech startup (Barrow) who built and operated a Next.js/TypeScript product with map-based public reporting, including clustering and dynamic geospatial loading to improve UX and performance. Also implemented a location-aware RAG chatbot using Pinecone, web scraping/transcription, caching, and fallback web search, and owned post-launch observability plus scaling decisions (load balancing/horizontal scaling) based on API usage patterns.”

Aashna Kunkolienker

Screened

Junior AI Engineer specializing in agentic workflows and ML platforms

San Ramon, CA2y exp

“Building a production LLM/agent system for a leading US dental provider that extracts rules from payer handbooks/portals and EDI 271 responses to validate and improve patient cost estimates. Combines GCP stack (BigQuery, GKE, Cloud Run, Pub/Sub, Vertex AI) with strong agent reliability practices (observability, validator agents, grounding, PII/hallucination guardrails, confidence scoring) and has led non-technical customer stakeholders on enterprise ServiceNow↔Aha sync and AI-powered enterprise search/summarization.”

Niteesh Ganipisetty

Screened

Mid-level AI/ML Engineer specializing in Generative AI, NLP, and Computer Vision

Grand Rapids, MI4y exp

“Built an LLM-powered learning assistant (EduQuizPro/EduCrest Pro) that uses RAG over URLs and PDFs to generate quizzes, notes, and explanations for students/professors. Emphasizes production robustness—implemented dependency fallbacks (FAISS/Sentence Transformers/Gradio), CLI-safe mode, and NumPy-based indexing—along with a custom orchestration layer to keep multi-step AI workflows reliable.”

Mounika Gunturu

Screened

Senior Python Full-Stack Developer specializing in cloud-native microservices and data platforms

New York, NY9y exp

“Backend/data engineer from Oliver Wyman who built and ran production Python (FastAPI) services on AWS (ECS/Lambda/API Gateway) supporting risk modeling and regulatory reporting. Strong in reliability/observability, Glue-based ETL with data quality controls, and legacy SAS-to-Python modernization with rigorous parity validation; also demonstrated measurable SQL performance wins and cost-control improvements in serverless scaling. Based in Raleigh, NC and can travel onsite for important Bethesda-area meetings.”

“Built and owned an end-to-end AV operations automation and dashboarding platform for USC event operations, used daily to coordinate hundreds of live events. Delivered a React/TypeScript full-stack system integrating Smartsheet APIs with strong reliability practices (typed contracts, validation/fallbacks, safe rollouts) and experience with queue-based microservice patterns (idempotency, retries, DLQs, monitoring).”

“Software engineer with experience spanning an SEL internship and Walmart, combining backend/data pipeline work (Python, Kafka, relational DBs) with DevOps practices (Docker, Grafana, GitHub/Jenkins CI/CD, GitOps). Notably contributed to a REST-to-GraphQL migration aimed at reducing cloud utilization and implemented testing strategies to validate the transition.”

Aarushi Mahajan

Screened

Mid-level AI/ML Engineer specializing in NLP, Generative AI, and MLOps

New York, USA4y exp

“Internship experience shipping production AI systems: built an end-to-end RAG platform (Python/FastAPI + LangChain/LangGraph + vector search) to answer support questions from unstructured internal docs, with a strong focus on hallucination prevention through confidence gating and rigorous offline/online evaluation. Also delivered an AI-driven personalization/analytics feature using an unsupervised clustering pipeline, iterating with PMs to align statistically strong clusters with actionable business segmentation.”

“Built and deployed an AI-powered insurance claims fraud platform end-to-end using Java/Spring Boot, Kafka, OpenAI, pgvector, and AWS EKS. Stands out for combining LLM/RAG architecture with production-grade scalability and observability, delivering measurable impact including 62% less manual review, 40% better fraud precision, 37% higher throughput, and 99.95% uptime.”

“Built end-to-end financial workflow platforms at Citi spanning React frontends, Spring Boot microservices, Kafka, Redis, and Oracle. Particularly compelling for teams needing someone who can modernize legacy systems into real-time architectures—the candidate cites a 48x throughput improvement from a batch-to-Kafka modernization effort.”

“Backend engineer with hands-on experience building event-driven Java/Spring Boot and Kafka systems, plus AI-assisted document-classification workflows in enterprise environments. Stands out for a thoughtful, risk-aware approach to AI: uses it to accelerate delivery, but emphasizes validation layers, confidence thresholds, observability, and human review before AI can affect downstream business actions.”

“AI/ML engineer in financial services who has built both LLM-powered compliance tools and production fraud/credit risk systems at Ally Financial. Particularly strong in regulated, high-stakes environments: combines RAG/LLM architecture, rigorous evaluation, and human-in-the-loop governance, and also helped stand up a unified ML platform from scratch.”

Javon Lee

Screened

Senior Software Engineer specializing in AI platforms and cloud-native systems

Baltimore, MD8y exp

“Engineer with startup CTO experience and recent hands-on full-stack work at Microsoft and Clarity, focused on compliance and AML workflow platforms for financial services. Stands out for building scalable data and audit systems that reduced manual processing and improved performance, while operating effectively in ambiguous early-stage environments.”

“Software engineer with Intuit experience shipping an end-to-end real-time financial insights product on AWS, using event-driven architecture with Kafka and Spark Streaming to process millions of records with low latency. Also delivers customer-facing React + TypeScript dashboards and has hands-on production operations experience, including resolving a database scaling incident via read replicas, query tuning, and connection pooling.”

“Backend engineer with Citigroup experience who built and evolved a self-service user provisioning/identity backend, cutting onboarding from 45 minutes to under 2 minutes. Demonstrates strong production-grade integration and reliability practices (isolated integrations, retries, rollback logic, heavy logging) plus secure API development in Python/FastAPI with OAuth scope-based authorization and incremental, low-risk rollout strategies.”

“Worked on Sophia the humanoid robot, building production animation pipelines and enhancing human-robot interaction via perception and behavior orchestration. Experienced in stabilizing noisy perception-driven state transitions and designing smooth, user-centered behavioral flows, collaborating closely with artists, animators, and experience designers to translate creative intent into measurable system behavior.”

“Backend engineer who built a Logistics-as-a-Service platform in Go, proactively refactoring a monolithic REST service into gRPC microservices to improve performance and maintainability. Led a 3-person team with disciplined code reviews, Dockerized DB migrations, and a canary-style rollout (5% traffic) monitored for latency and failures; also implemented JWT/OAuth2 RBAC and production-minded edge-case handling in an ordering system.”