Vetted Version Control Professionals

“Corporate strategy / M&A professional with experience acting as a chief-of-staff-style associate to multiple managing directors at Focus Financial Partners while running parallel M&A, enterprise vendor negotiations, and infrastructure strategy work. At BNY, led strategic growth initiatives around fintech partnerships (TAM + financial modeling) and supported a confidential divestiture using rigorous information-control processes.”

“Robotics software engineer who built and owned an autonomous laparoscope tracking system on a UR3e with an eye-in-hand RealSense camera, integrating YOLO-based tool detection with velocity control under a strict RCM constraint and deploying successfully in a hospital setting. Deep ROS2/MoveIt2 experience (architecture, QoS, custom nodes) plus autonomy stack work across SLAM, planning, and real-time latency/control debugging.”

“ML/LLM engineer building production, real-time fraud detection for financial transactions using a two-tier architecture (fast ML + GPT) to deliver both low-latency decisions and analyst-friendly risk explanations. Experienced orchestrating end-to-end retraining, drift monitoring, and automated model promotion with Databricks Jobs/Workflows and MLflow, and partnering closely with fraud analysts to tune alerts, thresholds, and dashboards.”

“ML/LLM practitioner with production experience building a healthcare review sentiment pipeline (RateMDs) using Hugging Face Transformers plus a LangChain+FAISS RAG layer for interactive querying. Also led orchestration-driven optimization of Nike’s Fusion ETL pipeline, improving runtime efficiency by 20%, and has experience translating ML outputs into Tableau dashboards for non-technical healthcare stakeholders (e.g., readmission risk).”

“At Citi, helped lead the productionization of an internal LLM-driven automation workflow into a production-ready developer platform, focusing on determinism/reproducibility, security, and cost controls. Implemented prompt versioning/registry, JSON schema validation, sanitization, and deep telemetry (including manual edit-distance) plus human-in-the-loop review and phased rollout—driving major SDLC efficiency gains (e.g., test script creation cut from ~1 week to ~1 day).”

“Player-coach engineering leader focused on consumer-grade video/multimodal products and high-reliability identity/auth experiences. Led design and implementation of multi-step mobile login/MFA flows with telemetry-driven funnel improvements, shipped Node services and security fixes, and owned auth incidents end-to-end using RUM and step-level instrumentation. Introduced feature-flagged delivery and targeted review/testing practices to speed iteration ~20–30% while keeping login stability high.”

Shanmukha Koganti

Screened

Mid-level AI/ML Engineer specializing in recommender systems and edge computer vision

Bay Area, CA6y exp

“ML/AI engineer with production experience at Shopify and Intel, building a deep learning product ranking system that lifted add-to-cart ~14% and serving real-time similarity search via FAISS+Redis under <20ms latency at massive scale. Also deployed computer vision models to 100+ retail edge locations using Docker/Ansible/k3s with zero-downtime rollouts, and applies strong MLOps practices (A/B testing, canary/shadow, observability) plus performance optimization (OpenVINO, INT8).”

Alex Vo

Screened

Staff Backend Software Engineer specializing in telemetry pipelines and observability

San Jose, CA3y exp

“Backend engineer from VMware focused on proprietary enterprise systems (monitoring tools, data pipelines, and APIs). Drove a ClickHouse migration POC (local to remote host) using a dual-write/cutover approach and source-level debugging across Node/driver differences during a Node 12→20 upgrade, and delivered measurable performance gains (~20% CPU/memory improvement) through batching and streaming ingestion.”

Amit Sharma

Screened

Principal Software Engineer specializing in AI/LLM platforms, payments, and healthcare systems

San Francisco, CA25y exp

“Engineering player-coach who recently shipped an agent-based workflow to extract key info from unstructured web data (browser agents + CDP) and populate daily digests/calendars, owning architecture through testing. Also built a Flask-based LLM evaluation and regression testing system using G-Eval/Confident AI dashboards, and applies a rigorous, research-driven approach to selecting third-party tools with stakeholder buy-in; has healthcare ops/onboarding workflow experience at Vivio Health.”

“LLM/agent engineer who has shipped multiple autonomous, multi-step agents to production (document-to-SOP conversion, test generation, code generation) using a custom Python DAG orchestrator with persistent state, tool-calling permissions, and structured outputs (Pydantic/JSON Schema). Demonstrates strong production hardening practices—semantic contracts, golden-dataset prompt regression tests, circuit breakers, and multi-level monitoring—and delivered large productivity wins (34 hours of manual writing reduced to ~20 minutes review; ~15–20 engineering hours/week saved).”

colton childrey

Screened

Senior QA Leader specializing in AAA games, XR platforms, and backend systems

Los Angeles, CA7y exp

“Game/interactive software QA professional with experience spanning AAA titles (Activision Black Ops 3; Halo 4/Forge) and VR at Meta (The World Beyond). Notable for modernizing QA operations at Lego BrickLink Studio by implementing Testmo and dramatically increasing test coverage and user satisfaction, plus quickly diagnosing and unblocking a critical public-launch issue caused by misconfigured gatekeeper flags.”

Vedant Kharwal

Screened

Intern AI/ML Engineer specializing in Generative AI and applied machine learning

Mumbai, India1y exp

“New graduate with hands-on LLM work building a RAG pipeline (HNSW, lexical reranking/boosting, ReAct) and optimizing it through ablation to dramatically reduce latency. Also building a modular personal assistant with a custom wake word model, router-driven agent selection, and integrations like Spotify with secrets managed via .env.”

Sirisha Maddikunta

Screened

Mid-level Generative AI Engineer specializing in enterprise LLM and healthcare AI solutions

O Fallon, MO6y exp

“Built and owned an end-to-end LLM-powered fraud investigation assistant that automated case summaries and risk analysis, cutting analyst investigation/documentation time by 40%. Stands out for translating RAG concepts into a production-grade internal platform with strong evaluation, monitoring, and reusable Python service architecture that improved both analyst trust and engineering velocity.”

“Unity VR/MR developer with multiple shipped Meta Quest projects, including a multi-user mixed-reality co-creation experience featuring real-time connection logic and shader-driven mesh generation for performance. Also builds AI-powered interactive applications, recently creating a video interview simulator using OpenAI API, HeyGen real-time avatars, and AWS audio/agent tooling while managing end-to-end latency challenges.”

“Software engineer focused on reliability and scalable systems: built React/TypeScript dashboards backed by Java/Spring Boot APIs and designed Kafka-based microservices with strong contract/versioning discipline. Known for shipping incremental improvements with tight feedback loops and for creating internal observability tools that streamline on-call and incident diagnosis under high-traffic conditions.”

“Data engineer with healthcare (CVS Health) experience who migrated production PySpark workloads to native BigQuery SQL and built a Great Expectations-based validation microservice on GKE (Flask + REST) integrated into Cloud Composer. Has operated high-volume pipelines (~300–400GB/day) and designed external vendor ingestion on AWS (Lambda/Step Functions/Glue) with schema-drift detection, alerting, and backfill-safe controls to protect downstream Snowflake/BigQuery tables.”

“Built and shipped a production enterprise RAG knowledge assistant that returns grounded, cited answers and uses confidence-based fallbacks (clarifying questions/abstention) with monitoring and compliance controls for sensitive data. Implemented end-to-end agent orchestration (function calling, structured JSON, state, retries/rate limits) plus eval/feedback loops, and achieved a reported 30–40% improvement in knowledge-task completion time while reducing hallucinations via retrieval improvements.”

Shriya Bannikop

Screened

Mid-level Software Engineer specializing in cloud platforms, data engineering, and distributed systems

Seattle, WA5y exp

“Full-stack engineer who built and owned an AI-assisted job-matching dashboard in Next.js App Router/TypeScript, keeping LLM logic server-side and improving performance via deduplication, caching/revalidation, and streaming (35% fewer duplicate LLM calls; 40% faster first render). Also has strong data/backend chops: designed Postgres models and optimized queries at million-record scale (1.8s to 120ms) and built durable AWS multi-region telemetry workflows with idempotency, retries, and monitoring.”

Yashwanth J

Screened

Mid-level Software Engineer specializing in LLM agentic AI and full-stack systems

Seattle, WA4y exp

“Full-stack engineer at Bank of America who built and iterated a real-time transaction monitoring/fraud detection system processing 50K+ daily transactions, improving latency (25%), dashboard performance (30%), and reducing manual investigation time (40%) while meeting PCI DSS via OAuth2 and RBAC. Also built a scalable ETL pipeline for messy financial data with strong reliability/observability (ELK, retries, DLQ), boosting data integrity from 87% to 99% and sustaining 99.8% uptime.”

“Built and shipped a production LLM-powered RAG assistant for enterprise internal document search (PDFs, knowledge bases, structured data), addressing real-world issues like noisy documents, hallucinations, and latency with grounded prompting, retrieval-confidence fallbacks, and performance optimizations. Also partnered with compliance and business teams at JPMc to deliver a solution aligned with regulatory constraints, supported by monitoring, feedback loops, and systematic evaluation.”

“Associate Director in commercial technology leading Salesforce platform delivery (Sales Cloud + Health Cloud) for patient engagement and order management. Personally led secure integrations like Bartender Cloud label/barcode generation (PDF creation, encryption, malware scanning) and owned a major StreamSets-based Salesforce data sync incident triggered by a Salesforce region move, adding proactive monitoring and automated DR/failover. Experienced in scaling delivery via CI/CD, release cadence, and leading teams through architecture reviews, code reviews, and lead-to-cash automation.”

Vivek Reddy

Screened

Mid-level Data Scientist/Data Engineer specializing in ML pipelines, insurance and healthcare analytics

Los Angeles, CA7y exp

“Built a production assistive-vision iPhone app to help visually impaired users find grocery items, training a custom YOLO detector on 2,000+ self-collected/annotated images and deploying via CoreML with a cloud multimodal LLM for navigation instructions. Brings hands-on AWS serverless + ECS container deployment (CDK/GitHub Actions) and a disciplined approach to AI workflow reliability (state-machine design, offline evals, stress tests, logging/metrics), plus experience communicating model insights to non-technical stakeholders (MOTER Technologies).”

“Game engineering leader/player-coach who recently shipped Netflix/Boss Fight’s mobile hit Squid Games Unleashed, owning live ops scheduling (XML/JSON to C# in Unity) and storefront integrations while reviewing 6–8 engineers’ code. Experienced in client-server reliability work (The Sims 4 time-sync incident) and in speeding delivery via process redesign (gated approvals), with a coaching style informed by 6 years teaching improv theater.”