Vetted Apache Spark Professionals

“Candidate is pursuing a CTO role and has helped multiple startups turn early technology concepts into concrete, real-world technical requirements. They cite a systems science and mathematics background, along with experience at JPMorgan Chase, and appear strongest in technical strategy, concept fleshing, and identifying strong people to help teams succeed.”

“AI/backend engineer who has owned production applied-ML systems end to end, including a Jitsi meeting intelligence platform with custom RoBERTa boundary detection, LLM summarization, and automated retraining from user feedback. Also has healthcare AI experience building a diabetes medication titration system with strict validation, drift monitoring, and safety guardrails—showing both product speed and high-stakes engineering rigor.”

“AI/LLM engineer with strong healthcare domain depth who has shipped production-grade agents for care coordination and clinical workflow automation. Stands out for combining Knowledge Graph RAG, LangGraph orchestration, and rigorous eval/guardrail systems to improve reliability in high-stakes environments, with measurable gains in review time, hallucination reduction, latency, and clinician adoption.”

“ML/AI engineer with hands-on ownership of a production LLM/RAG system at Goldman Sachs, focused on workflow automation and large-scale document search for operational teams. They combine strong MLOps and backend engineering skills with practical GenAI evaluation and safety practices, and cite measurable impact including 22% better task guidance accuracy and sub-second search across millions of records.”

“Data engineer with a Master's in Data Science who has owned 30+ customer-facing K-12 SIS migrations end-to-end, building ETL, validation, and SOP-driven deployment processes in a PII-sensitive environment. Also brings recent hands-on agentic AI experience from a biotech capstone, where they led a production-oriented NLP-to-SQL + RAG support system that handled about 30% of support queries in testing.”

“Early-career robotics engineer who has integrated LLM/NLP command interfaces (OpenAI/LLaMA) into ROS-controlled industrial manipulators and built data-driven controls for underwater soft robotic actuators. Combines hands-on fabrication (balloon actuator with embedded copper traces) with sensor debugging (IMU/Aurora) and simulation work in Gazebo, with practical exposure to edge deployment constraints on Jetson Nano and model quantization.”

“LLM/RAG practitioner who productionized an LLM-driven customer communication and transaction understanding system at PayPal, emphasizing privacy/compliance guardrails and large-scale data normalization. Experienced in real-time debugging of hallucinations via retrieval pipeline tuning and in leading hands-on developer workshops and sales-aligned POCs to drive adoption.”

“Built a production AI-driven QA automation platform using a multi-agent architecture (MCPs + LangGraph) to run parallel website tests across multiple device environments via automated image building and containerization. Currently collaborating with restaurant operators and managers to deliver an agentic restaurant analytics system, emphasizing deep domain discovery with non-technical stakeholders.”

“ML/NLP engineer with experience at Flatiron Health building a production NLP platform that processed millions of clinical notes, using BERT/BiLSTM-CRF and spaCy to extract and normalize entities from noisy EMR text with oncologist-in-the-loop validation. Also built scalable retail ML workflows (Spark + Kubernetes + feature store caching) and applied vector databases plus contrastive-learning fine-tuning to improve retrieval relevance and recommendations.”

“CTO who ties technology strategy directly to business outcomes, building multi-year roadmaps with measurable ROI. Led major modernization (cloud, data platform, unified API, microservices + CI/CD) delivering 5x faster releases/deployments, 99.8% uptime, and 40% user growth without headcount increases, while scaling engineering from 15 to 80+ in ~18 months.”

Pavan Kumar Malasani

Screened

Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and GenAI

Remote, USA4y exp

“GenAI/ML engineer in Citigroup’s finance environment who has deployed production RAG systems for investment banking under strict privacy and model-risk constraints. Built an internal-VPC Llama2 + Pinecone + LangChain solution with NER redaction and citation-based verification to prevent hallucinations, delivering major time savings, and also partnered with global finance executives to ship an AI early-warning indicator for treasury/liquidity risk.”

Dinesh Kumar Patibandla

Screened

Mid-level Machine Learning Engineer specializing in LLMs and RAG for finance and healthcare

Texas, USA4y exp

“ML Engineer with recent Goldman Sachs experience building and deploying a production RAG/LLM assistant for summarization, drafting, and internal knowledge retrieval across financial, risk, and compliance documents. Designed for heavy regulatory constraints and scaled to 10,000+ concurrent users using Kubernetes-based orchestration, dynamic LLM routing, and rigorous testing (adversarial prompts, A/B tests, load simulations) with privacy controls like differential privacy.”

Pandari G

Screened

Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems

San Francisco, USA5y exp

“GenAI/LLM engineer with production deployments in both fintech and retail: built an AI-powered mortgage document analysis/automated underwriting pipeline at Fannie Mae (OCR + custom LLM) cutting underwriting review from 3–4 hours to under an hour with privacy-by-design controls. Also helped build Sephora’s GenAI product advisory bot using LangChain-orchestrated RAG (Azure GPT-4, Azure AI Search, MySQL HeatWave vector search), focusing on grounding, evaluation, and compliance-aware architecture choices.”

HEMANTH KUMAR KOTTAPALLI

Screened

Mid-level Machine Learning Engineer specializing in GPU-accelerated LLMs and MLOps

GA, USA4y exp

“Built and deployed a production LLM-powered decision-support system for supply-chain planners that explains demand forecast changes using grounded retrieval from sales, promotion, inventory, and supplier data. Implemented strict anti-hallucination guardrails and latency optimizations, deployed as a real-time AWS API with monitoring, and reported ~15% forecast accuracy improvement and ~12% supply-chain risk reduction. Experienced orchestrating data/ML/LLM workflows with Airflow, LangChain/LangGraph-style patterns, and AWS Step Functions while partnering closely with non-technical business users via demos and example-based requirements.”

Sudhan Louis

Screened

Director of Enterprise Architecture specializing in digital transformation, AI, and API strategy

Rolling Hills Estates, CA26y exp

“Hands-on architect/technology leader who builds prototypes (including Agentic AI wellness/biomarkers) and then scales teams to execute. Led a ~$400M global e-commerce transformation spanning 95 countries with active-active US/EU multi-region resilience, microservices/MFE (MACH), and strong security patterns (service mesh + API gateway + Ping Identity), plus modern data foundations (customer hub/MDM/Snowflake, data fabric/medallion).”

Sachin Reddy Kunta

Screened

Mid-Level Backend Software Engineer specializing in payments, fraud systems, and AI agent infrastructure

San Francisco, CA3y exp

“Early-career engineer who owned an end-to-end objective assessment/coding contest platform at an edtech startup, using Postgres + S3 and Redis (queues + ZSET) to decouple and scale code submission processing with worker sandboxes. Also implemented idempotency controls and set up monitoring and CI/CD while the rest of the team focused on curriculum.”

“Data engineer at Capital One building AWS-based real-time and batch pipelines and backend data services for financial/fraud use cases. Has owned end-to-end pipelines processing millions of records/day, implemented dbt/Great Expectations quality gates, and tuned Redshift/Snowflake workloads (cutting query latency ~22–25% and reducing pipeline failures ~30–40%) while supporting 15+ downstream consumers.”

“Healthcare data engineer with hands-on ownership of claims/member data pipelines on a cloud analytics platform, spanning batch and streaming ingestion (Airflow/Kafka/Spark/Databricks) through serving for reporting. Emphasizes reliability and data quality via embedded validation, schema-drift detection, deduplication, and operational monitoring/incident response, plus pragmatic CI/CD and observability setup in early-stage/ambiguous projects.”

Akashreddy Madduri

Screened

Senior Backend Engineer specializing in real-time data platforms for FinTech and Healthcare

Plano, Texas6y exp

“Backend/data engineer with experience at JPMorgan building near real-time payment risk and fraud scoring pipelines using Python, Spark Structured Streaming, and Delta Lake, emphasizing auditability, security, and data correctness (dedupe/late events) to reduce false positives. Also led a legacy-to-cloud migration of claims/eligibility data at Cogna with parallel runs, phased rollout, and healthcare-specific validation (ICD-CPT mapping).”

Sanat Ahuja

Screened

Senior Engineering Manager specializing in platform, data/ML, and identity/access systems

Los Angeles, CA16y exp

“Senior engineering leader from Goodyear’s AndGo startup-like division who scaled the org from 12 to 30+ across pod-based teams and introduced an Architect Guild/ARD governance model. Led a 4-month Europe launch requiring AWS regional infrastructure, GDPR compliance, i18n/l10n, and new EMEA reporting pipelines, and has hands-on depth in API performance, incident response, and GraphQL/Hasura adoption to boost product velocity.”

Sushma Mangalampati

Screened

Mid-level Data Engineer specializing in lakehouse ETL and analytics engineering

Boston, MA6y exp

“Data engineer with strong end-to-end ownership of production lakehouse pipelines (Snowflake + Databricks + Airflow + dbt + Great Expectations), handling 8M+ records/month and 500K+ daily CDC updates. Delivered measurable reliability and efficiency gains (41% cost reduction, freshness improved from 4h to 30m, 35% fewer downstream incidents) and has experience building a lakehouse platform from scratch across 12 source systems.”

Zhiwen Zhao

Screened

Junior Data Engineer specializing in cloud ETL and big data platforms

New York, NY3y exp

“Data engineer focused on transit/transportation datasets, building Spark-based pipelines that ingest from Oracle/APIs, apply PySpark data-quality fixes, and publish star-schema fact tables to Azure Data Lake. Experienced troubleshooting complex Spark failures (using checkpointing to manage long lineage) and operating Airflow-driven backfills and GitLab CI deployments for production DAGs.”

“Built internal production LLM tools for engineering and support, including a customer-health assistant and a RAG-based incident explainer grounded in logs, metrics, and deploy data. Stands out for combining strong GenAI safety/evaluation practices with pragmatic backend engineering, delivering measurable impact like a 40% drop in data-help requests and answers in seconds instead of minutes or hours.”

“ML/AI engineer with hands-on experience building production healthcare AI systems across predictive modeling and GenAI. They built an end-to-end patient risk prediction platform and a RAG-based clinical summarization feature, combining strong NLP/LLM skills with AWS deployment, monitoring, drift detection, and reusable Python service design to deliver measurable clinical and operational impact.”