Vetted Apache Spark Professionals

“Hands-on engineering manager at a marketing company leading a highly senior, distributed team (10 direct reports) while personally coding ~60–70% and owning end-to-end architecture across three interconnected products. Built agentic CRM automation and a reinforcement-learning-driven distribution layer for channel spend/bidding, with a strong focus on scalable design and observability (Prometheus/APM/logging) enabling frequent releases and few production incidents.”

Vigynesh Bhatt

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in backend, cloud, and ML systems

Salt Lake City, UT4y exp

“Software engineer with experience across Goldman Sachs, BYU Broadcasting, Juniper Networks, and an edtech startup (Doubtnut), spanning data migrations, AWS-based media backends, and microservices observability. Built a Redis/ElastiCache caching layer in front of DynamoDB/S3 to improve media delivery latency and cost, and created an SEO indexing automation tool using the Google Search Console API that saved ~15–30 person-hours per day.”

Venu Dave

Screened ReferencesStrong rec.

Junior Software Development Engineer specializing in backend data platforms and LLM applications

New York, NY3y exp

“Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.”

Andrew Fares

Screened ReferencesStrong rec.

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

Seattle, WA4y exp

“Built production AI systems inside AWS finance/procurement, including an LLM-based supplier quote classification and price-vetting workflow that drove $5M in savings over 3 months. Combines GenAI evaluation expertise, internal platform design, and reusable Python data-quality tooling with strong cross-functional execution across finance, accounting, and hardware engineering.”

“Engineering leader (12+ years Director, 17 years lead) focused on developer productivity and platform/framework work across Oracle, PlayStation, Workday, and CafePress. Notable for building distributed teams from scratch and delivering high-impact platform architecture—e.g., re-architected PlayStation’s upload pipeline to support 500GB–5TB submissions using browser-to-AWS chunked uploads with SNS/SQS and deduplication/resume support.”

“ML/LLM engineer who has built and productionized a large multimodal LLM pipeline end-to-end—fine-tuning a 20B+ parameter model with distributed/FSDP training and deploying on Kubernetes via Triton for ~5x throughput. Strong focus on reliability and safety (monitoring with SHAP, guardrails, A/B testing) with reported ~22% relevance lift and reduced harmful/incorrect outputs, plus experience orchestrating ETL/retraining workflows with Airflow across S3/Snowflake/RDS.”

“Built and deployed a production LLM-powered clinical support assistant at BJC HealthCare (RAG + transformer) to answer patient questions, summarize clinical notes, and support appointment workflows. Implemented PHI-safe data pipelines (Spark/Hadoop/Kafka) with automated scrubbing, dataset versioning, and audit logs, and runs the system on Docker/Kubernetes with Pinecone vector search while partnering closely with clinical operations staff.”

“Amazon Alexa engineer who architected and shipped a GenAI Knowledge Agent used by 2M+ customers, focused on making LLM outputs auditable via citations and a verification layer that prevents hallucinations. Built the full vertical slice (FastAPI/LangChain backend + React/TypeScript streaming UI) while keeping p99 latency under 200ms, and has proven incident response experience on AWS (Lambda/DynamoDB scaling issues).”

“Python backend engineer focused on high-throughput document/PDF processing systems, building end-to-end pipelines that extract structured content for downstream NLP use cases. Demonstrates strong practical MLOps-adjacent infrastructure skills: Kubernetes deployments, GitLab CI, GitOps workflows, and an incremental migration to AWS using EC2/Lambda tradeoffs. Deep hands-on optimization experience (selective OCR, layout-aware extraction, parallelism, caching, idempotency, and backpressure/autoscaling).”