Vetted Apache Spark Professionals

“Data engineer with experience across healthcare and geospatial risk systems, owning end-to-end pipelines from ingestion through serving on AWS/Azure stacks. Built HIPAA-compliant data quality gates and CDC for millions of daily claims, and also delivered a real-time wildfire risk platform with 20-minute refresh cycles and a 60% data accuracy lift. Strong in streaming (Kafka), Spark performance tuning, and production-grade orchestration/CI/CD (Airflow, Docker, Jenkins, GitHub Actions, Terraform).”

“Data engineer at CenterPoint Energy who built and operated multiple production-grade GCP data systems: a daily Snowflake→BigQuery replication framework (150+ tables) with Monte Carlo/Atlan-driven observability and schema-drift protection, plus a FastAPI metrics service for pipeline health. Demonstrated measurable impact (40% faster dashboard queries, 70% less manual refresh work, zero data loss) and strong operational rigor (scaling Cloud Run jobs, SAP SLT reconciliation, quarantine patterns, CI/CD via GitHub Actions + Terraform).”

Rushir Bhavsar

Screened

Intern AI/ML Engineer specializing in LLMs, MLOps, and distributed training

1y exp

“Founding AI engineer (June 2024) at Talon Labs who built and productionized an LLM-powered chatbot for interacting with proprietary supply-chain documents, deployed at large scale (25–100,000 users). Experienced with RAG/LLM orchestration (LangChain, LlamaIndex, Groq AI) and production ops tooling (Kubernetes, Docker, Kubeflow, Airflow), with a metrics-driven approach to evaluation, observability, and stakeholder alignment.”

Sri Lalitha

Screened

Senior Full-Stack Java Engineer specializing in cloud-native microservices and FinTech

California, USA6y exp

“Backend engineer who owned a Python task management API with JWT auth, async notifications, and performance work (DB optimization/caching) to handle high volumes. Led an on-prem to Azure private cloud migration at Morgan Stanley using GitOps and IaC (Terraform/ARM) with phased rollout and rollback planning. Also built a Kafka real-time streaming pipeline with exactly-once/idempotent consumers and Prometheus/Grafana monitoring.”

Yijun Chen

Screened

Senior Full-Stack Software Developer specializing in IoT and cloud systems

Toronto, ON4y exp

“Frontend-focused engineer who built a full movie recommendation system from concept to production, comparing classic collaborative filtering with LLM-based recommendation approaches on AWS. Emphasizes scalable architecture, strict TypeScript data contracts, and high-quality Next.js/React UI patterns (defensive states, scoped state management, performance optimization) with disciplined QA and feature-flagged rollouts.”

Sana Khan

Screened

Mid-level AI/ML Engineer specializing in MLOps, LLMs, and real-time inference in FinTech

Oklahoma, USA4y exp

“ML/LLM engineer who has deployed a production LLM-powered assistant for intent classification and query routing (order recommendation/support deflection), combining BERT fine-tuning with an embedding-based retrieval layer and optimizing for low-latency inference. Experienced with end-to-end reliability practices—Airflow-orchestrated ETL, data validation/alerting, MLflow experiment tracking, and iterative improvements driven by user feedback and monitoring.”

Chandra Shekar Akkandra

Screened

Mid-level AI/ML Engineer specializing in fraud detection and risk analytics in Financial Services

Newark, CA5y exp

“Finance-domain ML/LLM engineer who has shipped production systems including a RAG-based financial insights assistant with a custom post-generation validation layer that verifies atomic claims against retrieved source text to prevent hallucinations in compliance-critical workflows. Also built large-scale MLOps automation on AWS using Kubeflow + MLflow + CI/CD for fraud detection and credit risk models processing 500M+ transactions/day with a 99.99% uptime goal, and partnered closely with JP Morgan risk/compliance stakeholders on NLP-driven compliance monitoring.”

“Has worked across multiple startup stages from pre-funding through Series D and emphasizes rigorous idea validation through direct conversations with both end users and purchasing decision-makers. Interested in applying NLP to automate summarization/abstracting of highly technical articles, with a balanced view of entrepreneurship that prioritizes health and family.”

Chakravarthy V P

Screened

Executive AI Consultant/CTO specializing in Agentic AI, GenAI, and cloud-native data platforms

Texas, USA21y exp

“Bootstrapped founder and CTO of C4Scale, a 2.5-year-old services-led company delivering MVP-to-scale product/platform builds for high-value clients across 5+ countries (10+ projects). Strong fit for roles blending scalable SaaS platform engineering, technical org leadership, and practical AI adoption, with clear awareness of the operational and GTM challenges of scaling into enterprise.”

“Hands-on implementation-focused candidate with experience owning cloud deployments and putting LLM/RAG workflows into production. They stand out for combining customer-facing deployment ownership with practical AI systems work, including retrieval tuning, hallucination mitigation, production incident response, and document-processing pipelines for messy real-world inputs.”

“Built and deployed a production RAG-based internal knowledge assistant that let analysts query company documents in natural language, using LangChain/LangGraph with Pinecone and a FastAPI service for integration. Emphasizes reliability in production through hallucination mitigation (retrieval tuning + prompt guardrails) and measurable evaluation/monitoring (accuracy, latency, task completion, hallucination rate), iterating based on user feedback.”

Rohit Vibhu Channananjundarya

Screened

Mid-level Software Engineer specializing in distributed systems and full-stack platforms

Chicago, IL6y exp

“Engineer who treats AI as a force multiplier rather than a replacement for judgment, with hands-on experience using tools like Claude Code, Cursor, Copilot, and Codex across planning, coding, testing, and review. Particularly notable for building a multi-agent PR review system that automated summarization, risk scanning, schema validation, and test suggestions, helping the team shift reviewer time toward architecture and business logic.”

Hilary Lutz

Screened

Intern IT and cybersecurity professional with data and Python skills

Philadelphia, PA5y exp

“Internship experience at Arkema and Proscia focused on improving onboarding and internal automation workflows. Built SQL-based processes for computer onboarding and security compliance checks, redesigned cybersecurity onboarding for different departments, and created templated setup instructions with GitHub-based review safeguards.”

“Currently building a DBC (Digital Birth Certificate) agentic AI system to speed root cause investigation for quality issues at their company. They bring hands-on experience designing and leading multi-agent workflows, including orchestrator/root-agent patterns, evaluation agents, clarification agents, and practical guardrails for hallucination, bias, and rate-limit management.”

“AI/full-stack engineer in gaming analytics who joined Omnic.ai at a 2-person stage, helped grow with the company, and built both backend and frontend for real-time gameplay analysis products. He combines computer vision production experience with LLM/RAG systems work, and has already led 4 employees while shipping 12 models in a fast-moving startup environment.”

“Backend-leaning full-stack engineer who has shipped real-time, customer-facing dashboards and ticketing/payment features at Freshworks and Global Payments. Strong in Python API design (Django/Flask/FastAPI) and React/TypeScript UIs, with hands-on experience scaling PostgreSQL for high transaction volumes and operating services on AWS, including incident response and HIPAA-aligned security controls.”

“At KPMG, built and productionized a secure RAG-based LLM assistant that lets business and risk stakeholders query data warehouses in natural language, reducing dependence on data engineers for ad-hoc analysis. Demonstrates strong production rigor (Airflow orchestration, CI/CD, containerization), retrieval/embedding tuning (rechunking, semantic abstraction for structured data), and reliability controls (confidence thresholds, refusal behavior, monitoring and canary evals).”

“Open-source JavaScript contributor focused on performance and maintainability in data visualization libraries—refactored legacy ES5 into modular ES6, added tests/docs, and delivered ~30% faster load times with positive community adoption. Also optimized a React dashboard (~40% load-time reduction) and took ownership in an ambiguous AI product initiative by setting milestones, standing up an initial ML pipeline, and shipping a prototype in ~6 weeks that became the basis for production.”

“ML/GenAI engineer with production experience building an LLM-powered governance layer that summarizes verified drift/performance signals into validation reports and release notes, designed for regulated environments with de-identification and non-blocking fallbacks. Strong Airflow-based orchestration background across healthcare and finance, integrating Databricks/Spark and MLflow for scalable retraining/monitoring. Demonstrated ability to partner with non-technical healthcare operations teams to deliver actionable risk-scoring outputs via dashboards and automated reporting.”

“Built production LLM systems including a real-time customer feedback analysis and workflow automation platform using RAG and multi-agent orchestration with confidence-based human escalation, addressing privacy and legacy integration challenges. Also automated ML operations with Airflow/Kubernetes (e.g., daily churn model retraining) cutting retraining time to under 30 minutes, and demonstrates a rigorous testing/monitoring approach plus strong non-technical stakeholder collaboration.”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“GenAI/LLM ML engineer (currently at Webprobo) building an enterprise GenAI platform with document intelligence and automation on AWS and blockchain. Has hands-on experience with RAG, LLM evaluation tooling, and orchestrating production LLM workflows with Apache Airflow, plus deep exposure to reliability challenges in globally distributed/edge deployments. Also partnered with business/marketing stakeholders at a banking client to deliver an AI-driven customer retention insights solution.”

“Data engineer focused on reliable, scalable analytics pipelines and external data collection. Has owned end-to-end pipelines processing 5–10M records/day, serving Snowflake data marts to Power BI/Tableau, and reports ~99% reliability through strong validation/monitoring. Also shipped versioned REST APIs for curated data with query optimization and caching.”