Vetted Apache Spark Professionals

Harideep Balusa

Screened

Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems

USA6y exp

“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”

Ojasmitha Pedirappagari

Screened

Mid-level AI Engineer specializing in LLMs, RAG, and agentic platforms

Jersey City, NJ5y exp

“Built and shipped a production RAG-based assistant that lets parents ask natural-language questions about their child’s learning progress, using pgvector retrieval (child-id filtered) and Redis caching to hit ~180ms latency. Implemented real-world guardrails and compliance (Llama Guard, COPPA, retrieval thresholds, fallbacks) with 99.5% uptime, and ran human-in-the-loop eval loops that improved satisfaction from 3.8 to 4.2 while serving 60k+ monthly users and reducing costs significantly.”

Srilekha Jakkula

Screened

Senior Data Engineer specializing in scalable data pipelines and API-driven data services

Chicago, IL5y exp

“Data engineer focused on building scalable, reliable end-to-end data pipelines and backend REST data services, spanning API ingestion plus batch/stream processing with Airflow, Kafka, Spark/PySpark, and SQL. Emphasizes strong data quality validation, monitoring/fault tolerance, and performance tuning for large datasets, with experience deploying in cloud environments using containerization and CI/CD.”

“Healthcare analytics candidate with hands-on experience turning messy EHR, billing, and operational data into validated SQL datasets and automated Python/Airflow pipelines. They appear strongest in hospital KPI reporting—especially length of stay, readmissions, retention, and bed utilization—and have owned projects from metric definition through Power BI delivery and impact measurement.”

“Senior data scientist with ~5 years’ experience building production ML/NLP systems in finance (Wells Fargo) and deep learning for sensor analytics in connected vehicles (Medtronic). Has delivered end-to-end platforms combining time-series forecasting with transformer-based NLP, including automated drift monitoring/retraining (MLflow + Airflow) and standardized Docker/CI/CD deployments; achieved a reported 22% precision improvement after domain fine-tuning.”

“Backend/ML systems engineer who built a Flask + PostgreSQL internal ticketing platform and demonstrates strong database/ORM performance depth (indexes, partitioning, RLS multi-tenancy). Notably optimized a high-throughput attachment OCR/embedding pipeline with batching, deduplication, and Redis caching, cutting median latency from 45s to 10s and reducing worker cost by 35% while increasing throughput 4x.”

“AI/full-stack product engineer currently owning Fleck Intelligent Survey Chatbot at E15, a production RAG analytics assistant embedded in Compass Group dashboards for 300+ field operators. Stands out for combining LLM orchestration, analytics engineering, and strong systems thinking—cutting hallucinated numeric answers from 14% to 2%, reducing backlog 62%, and previously delivering a low-level protocol redesign at Amadeus that cut P99 latency by 56%.”

“Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.”

“BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.”

“Growth/creative marketer from Esleydunn Games who uses Google Analytics to integrate cross-channel performance data (TikTok, YouTube, LinkedIn, Facebook) and run structured A/B tests on video ad length and layout. Reported reducing CPA by 20 per customer when leveraging YouTube and TikTok, and improved CTR through CTA/button placement testing and ongoing user-feedback loops (forum/WeChat topics).”

“LLM/agentic workflow engineer with healthcare domain experience who built a HIPAA-compliant multi-agent RAG system for clinical review automation at UnitedHealth Group, achieving 92% precision and cutting latency 40% through async orchestration and Redis semantic caching. Also has strong data engineering orchestration background (Airflow on AWS EMR with Great Expectations) and a proven clinician-in-the-loop feedback process that improved model faithfulness by 18%.”

“Full-stack engineer focused on productionizing LLM applications, including an Android privacy-policy risk summarization app (Kotlin/React Native + FastAPI + Ollama) that cut response times from ~10s to ~5–6s via batching, caching, async, and event-driven architecture. Currently at PRGX building an LLM-based legal contract clause extraction system, partnering closely with legal/procurement SMEs to create schemas, labeled datasets, and evaluation pipelines that improved accuracy from 70% to 85%. Also has experience architecting real-time voice/LLM systems with streaming microservices (Kafka, Kubernetes, gRPC/WebSockets) and an avatar chatbot pipeline (TalkingHead, Google TTS, AnythingLLM).”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

“Built and shipped production generative AI/RAG assistants in regulated financial contexts (S&P Global), automating compliance-oriented Q&A over earnings reports/filings with grounded answers and citations. Experienced across the full stack—AWS-based ingestion (PySpark/Glue), vector retrieval + LangChain agents, GPT-4/Claude model selection, and production reliability (monitoring, caching, retries) plus rigorous evaluation and regression testing.”

“AI/ML Engineer with 4+ years of experience (Capital One, Odin Technologies) and a master’s in Data Analytics (4.0 GPA) who has deployed LLM/RAG systems to production for compliance/risk and document review. Strong in orchestration and MLOps (Airflow, Kubernetes, MLflow, GitHub Actions) and in tackling real-world LLM constraints like latency, context limits, and data privacy, with measurable impact (20%+ manual review reduction; 33% faster release cycles).”

“Customer-facing data engineering professional who builds and deploys real-time reporting/dashboard solutions, gathering reporting and compliance requirements through direct stakeholder engagement. Experienced with Google Cloud IAM governance, secure integrations (encryption, audit logging), and fast production troubleshooting of ETL/pipeline failures with follow-on monitoring and automated recovery improvements; motivated by hands-on, travel-oriented customer work.”

Daniel Berhane Araya

Screened

Senior AI/ML Engineer specializing in production-grade LLM systems for regulated finance

Fairfax, VA9y exp

“AI/LLM engineer with published work who built FinVet, a production financial misinformation detection system using multi-pipeline RAG, confidence-based voting, and evidence-backed outputs (F1 0.85, +37% vs baseline). Also built NexusForest-MCP, a Dockerized Model Context Protocol server exposing structured global deforestation/carbon data via SQL tools for reliable LLM tool use. Previously delivered borrower risk-rating (PD) models at BMO Financial Group that were validated and integrated into an enterprise credit system through close collaboration with credit officers and portfolio managers.”

maheen Adeeb

Screened

Senior Machine Learning Engineer specializing in LLMs, speech AI, and RAG systems

Chicago, IL3y exp

“AI engineer with production experience building multilingual speech-to-speech translation pipelines (ASR + LLM) for enterprise/media, focused on reliability at scale. Has hands-on orchestration experience (including IBM Watson contexts) and emphasizes production evaluation/monitoring using a mix of traditional metrics and LLM-based evaluators to catch quality regressions while balancing latency and cost.”

Hritvik Gupta

Screened

Mid-level AI Engineer specializing in LLMs, RAG, and healthcare AI

San Francisco, CA3y exp

“Built and scaled an AI-powered voice/chat patient engagement platform at Penn Medicine from early prototype into production clinical workflows, focusing on latency, edge cases, and user trust. Strong in LLM reliability engineering (structured prompts, validation/fallbacks), real-time troubleshooting with observability, and cross-functional enablement through pilots, demos, and sales/customer partnership.”

Mohithkumar Bolisetti

Screened

Mid-level Java Full-Stack Developer specializing in microservices and cloud platforms

Strongsville, Ohio5y exp

“Full-stack engineer focused on modernizing legacy financial/compliance platforms into cloud-native, domain-driven microservices. Deep hands-on experience across Spring Boot/Kafka/Redis/Postgres-Mongo backends and React/Angular frontends, with strong CI/CD and Kubernetes/OpenShift deployment practices for real-time, high-volume workloads.”

Bala Venkateswarlu K

Screened

Mid-level Data Scientist specializing in Generative AI, NLP, and MLOps

USA5y exp

“Built and deployed an LLM-powered claims-document summarization system (insurance domain) that cut agent review time from 4–5 minutes to under 2 minutes and saved 1,200+ hours per quarter. Hands-on across orchestration and production infrastructure (Airflow retraining DAGs, Kubernetes, SageMaker endpoints, FastAPI) and recent RAG workflows using n8n + Pinecone, with a strong focus on reliability, cost, and explainability for non-technical stakeholders.”

“Analytics professional at Optum with hands-on experience turning messy healthcare claims data from SQL, Excel, and CRM systems into validated reporting datasets and Power BI dashboards. They also built reproducible Python workflows for claims analysis and owned an end-to-end project focused on improving claims processing efficiency through metric design, segmentation, and stakeholder-driven operational improvements.”

“Built a production, real-time clinical documentation system at HCA that converts doctor–patient conversations into structured clinical summaries using speech-to-text, LLM summarization, and RAG. Demonstrated measurable gains from medical-domain fine-tuning (clinical concept recall +18%, ROUGE-L 0.62 to 0.74) while meeting HIPAA constraints via PHI anonymization and encryption, and deployed via Docker/FastAPI with CI/CD and monitoring.”

John Chance

Screened

Senior Machine Learning Engineer specializing in conversational AI and healthcare ML

Greenwood, LA9y exp

“ML/AI engineer with hands-on ownership of both classical recommender systems and safety-sensitive LLM agent platforms. They combine production MLOps depth with behavioral health domain experience, including clinical safety validation, explainability, and multi-agent orchestration, and cite measurable impact in both business metrics and latency reduction.”