Vetted Bitbucket Professionals

“Built and deployed a production Retrieval-Augmented Generation (RAG) platform in a healthcare setting to automate clinical documentation review and summarization, targeting near-real-time, explainable outputs. Emphasizes grounded generation to reduce hallucinations, latency optimizations (chunking/embedding reuse), and PHI-safe workflows with access controls, plus strong orchestration experience using Apache Airflow.”

“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).”

“ML/NLP engineer currently working in NYC on a system that connects complex unstructured data sources to deliver personalized insights, using embeddings + vector DB retrieval and a RAG architecture (LangChain, Pinecone/OpenSearch). Strong focus on production constraints—especially low-latency retrieval—using FAISS/ANN, PCA, index partitioning, and Redis caching, plus PEFT fine-tuning (LoRA/QLoRA) and KPI/SLA-driven promotion to production.”

“Software engineer with strong compliance-domain experience who built a customer-facing compliance and reporting dashboard using React/TypeScript with Spring Boot microservices. Demonstrates mature production engineering practices—contract-first APIs, event-driven architecture (Kafka/RabbitMQ), caching (Redis), and robust CI/CD + observability (Prometheus/Grafana/ELK)—and also created a Python-based audit automation tool adopted into the standard release process.”

“Full-stack engineer with production experience building and owning high-traffic e-commerce checkout flows in Next.js (App Router) + TypeScript across microservices (REST/GraphQL). Demonstrated measurable performance wins (30% checkout improvement; 85% initial load reduction at 20th Century FOX) and strong production rigor (APM/logs, CloudWatch, Postgres indexing + EXPLAIN ANALYZE), including offloading PDF generation to AWS Lambda.”

Jahnavi Bellapukonda

Screened

Mid-Level Software Engineer specializing in full-stack web and cloud systems

Remote, USA3y exp

“Full-stack engineer with strong data engineering and privacy-domain experience, having owned an automated Data Subject Rights (DSR) processing pipeline end-to-end across Azure SQL and GCP (GCS/BigQuery). Emphasizes production reliability via idempotency, validation checkpoints, structured logging/monitoring, and safe CI/CD-driven deployments, and has also built React+TypeScript + Node/Postgres web apps with scalable, maintainable architecture.”

Sandeep Mekala

Screened

Director-level Mobile Engineering Manager specializing in Generative AI and agentic mobile experiences

Remote13y exp

“iOS player-coach who led end-to-end development of real-time customer support chat and unified notification systems for T-Mobile’s iOS app using SwiftUI, Firebase, WebSockets, and Core Data (including offline handling). Drove measurable reliability/latency gains (~30%) through a major notification refactor and owned a high-severity push-notification incident from rollback through RCA and backward-compatible hotfix, while also scaling team process and people management.”

Shravya Shashidhar

Screened

Intern Software Engineer specializing in LLM agents and full-stack development

Seattle, USA1y exp

“Embedded C++ engineer with Bosch automotive infotainment experience, owning real-time audio middleware modules with strict latency/memory constraints. Strong in profiling/optimizing deterministic behavior, debugging hardware-specific intermittent issues, and building automated test + CI pipelines; currently ramping up on ROS2 concepts (DDS, nodes/topics/services) to transition toward robotics.”

Neeshma Narahari

Screened

Mid-level Software Developer specializing in backend microservices and cloud platforms

Irving, TX6y exp

“Full-stack product engineer with strong React and TypeScript depth who has owned dashboard features end-to-end, from UI architecture and rendering optimization through Spring Boot APIs and database query tuning. Particularly compelling for startup or high-growth teams: they’ve shipped 0→1 internal operations platforms, prioritized MVP workflows effectively, and iterated post-launch using user feedback, logs, and usage metrics.”

“Python backend engineer with experience combining traditional API/microservices development and GenAI integrations, including healthcare claims workflows. Particularly compelling for teams building production AI systems: they pair hands-on work with LLMs, RAG, LangChain-style orchestration, and AWS deployment with a strong emphasis on reliability, security, and engineering discipline.”

“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 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.”

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“Built and shipped a production LLM-powered personalized client engagement assistant in the financial domain, balancing real-time recommendations with strict privacy/compliance requirements. Demonstrates strong MLOps/LLMOps depth (Airflow + MLflow, containerized microservices, drift monitoring) and a privacy-by-design approach validated in collaboration with risk and compliance teams.”