Vetted Scala Professionals

Saiteja Gaddam

Screened

Mid-Level Data Engineer specializing in cloud data platforms and streaming analytics

3y exp

“Data engineer (Intuit) who owned an end-to-end telemetry and subscription analytics platform processing ~22M events/day, built on Kinesis/S3/Glue/Spark/Airflow/Redshift. Strong focus on reliability and data quality (schema drift controls, quarantine layers, idempotent reruns) and performance tuning, achieving a reporting latency reduction from ~15 minutes to under 4 minutes while enabling revenue and churn analytics for business teams.”

“Full-stack engineer with experience spanning Amazon IMDb and Northeastern’s NeuroJSON portal, combining consumer product work with complex scientific data applications. Built IMDb’s streaming providers feature—described as the company’s most impactful feature of 2023—and has hands-on experience with React/Angular, GraphQL, AWS, Python services, and production monitoring.”

“ML/LLM engineer building production, real-time fraud detection for financial transactions using a two-tier architecture (fast ML + GPT) to deliver both low-latency decisions and analyst-friendly risk explanations. Experienced orchestrating end-to-end retraining, drift monitoring, and automated model promotion with Databricks Jobs/Workflows and MLflow, and partnering closely with fraud analysts to tune alerts, thresholds, and dashboards.”

“Senior data engineer with experience in both healthcare (CVS Health) and financial services (Bank of America), building large-scale Azure lakehouse pipelines (30+ EHR sources, ~5TB) and real-time streaming services (Event Hubs/Kafka) for patient vitals. Strong focus on reliability and data quality (Great Expectations, monitoring/alerting, schema drift automation), with measurable outcomes like 50% runtime reduction and 99%+ uptime for regulatory reporting pipelines.”

“Data engineer with Intuit experience owning end-to-end, high-volume financial data pipelines (API/S3 ingestion, Airflow orchestration, Spark/PySpark + SQL transforms, Snowflake marts). Strong focus on reliability and data quality—achieved 99.8% SLA and cut discrepancies by 35% using Great Expectations, reconciliation, schema versioning, and automated backfills; also built near real-time Kafka/API data services with CI/CD and observability.”

“Data/Backend engineer with experience at Naukri building large-scale analytics products over a 130M+ user base, including Spark/Airflow pipelines and Kafka-based clickstream validation with Confluent Schema Registry. Also built an audience segmentation backend (Athena/S3 + Spring Boot APIs) for non-technical internal teams and recently shipped a GenAI customer data audit system (FastAPI/Postgres/Llama) that cut sales-planning validation from ~3 months to ~1 week.”

Harsh Sanas

Screened

Intern-level Software Engineer specializing in GenAI, RAG, and backend systems

San Francisco, CA2y exp

“AI/LLM engineer focused on shipping production-grade agents that automate support, sales intake, and ERP-connected workflows. Stands out for combining strong orchestration and guardrails with measurable business outcomes, including 45% faster support handling, ~$1.2M annual savings, 18% higher customer satisfaction, and 99.5%+ reliability in production.”

“Built and owned end-to-end production systems for a healthcare platform, including a predictive task recommendation feature (React + FastAPI + ML on AWS ECS) that cut backlog 20% and saved coordinators ~10 hours/week. Also productionized an AI-native RAG system (vector DB + LLM) delivering 40% faster query resolution, and led phased modernization of a monolithic FastAPI service into async microservices using feature flags and canary releases.”

“LLM/agent engineer who has shipped multiple autonomous, multi-step agents to production (document-to-SOP conversion, test generation, code generation) using a custom Python DAG orchestrator with persistent state, tool-calling permissions, and structured outputs (Pydantic/JSON Schema). Demonstrates strong production hardening practices—semantic contracts, golden-dataset prompt regression tests, circuit breakers, and multi-level monitoring—and delivered large productivity wins (34 hours of manual writing reduced to ~20 minutes review; ~15–20 engineering hours/week saved).”

Felix Li

Screened

Intern Software Engineer specializing in data pipelines and full-stack web development

New York, NY1y exp

“Internship at Radar (geolocation infrastructure) where they owned automation of multiple geospatial data ingestion pipelines (including US/Canadian address ingestion), orchestrating Spark (Scala) jobs via Python-based Airflow and using GitOps-style CI/CD workflows.”

“Built and owned a production conversational AI platform for a healthcare contact center, including RAG-based agent assist, hybrid retrieval, safety guardrails, and production monitoring. Stands out for combining LLM product delivery with strong operational rigor, driving a reported 25-30% improvement in handling time in a sensitive healthcare environment.”

“Built a production-style PyTorch LSTM system that generates structured piano compositions from 1200+ MIDI files, then significantly improved long-range musical coherence by implementing Bahdanau attention based on research literature. Also has internship experience using Docker Compose for containerized backend workloads and has independently used Ray to scale ML experiments across multiple GPUs, including dealing with GPU scheduling/memory oversubscription issues.”

“Backend/platform engineer with payroll domain depth who built high-volume payroll processing microservices (Java/Spring Boot, Kafka, PostgreSQL, Redis) on AWS Kubernetes and debugged major peak-cycle latency by redesigning transaction boundaries and moving to async Kafka processing (>50% latency reduction). Also shipped an LLM-powered HR assistant using RAG with strong security/guardrails (RBAC, PII masking, audit logs) that cut support tickets by 40%, and designed reliable multi-step agent workflows with retries, circuit breakers, and idempotency.”

“Data engineer with healthcare (CVS Health) experience who migrated production PySpark workloads to native BigQuery SQL and built a Great Expectations-based validation microservice on GKE (Flask + REST) integrated into Cloud Composer. Has operated high-volume pipelines (~300–400GB/day) and designed external vendor ingestion on AWS (Lambda/Step Functions/Glue) with schema-drift detection, alerting, and backfill-safe controls to protect downstream Snowflake/BigQuery tables.”

Bhanu Chander

Screened

Senior Data Engineer specializing in cloud data platforms and real-time pipelines

New York, NY6y exp

“Data engineer focused on reliability and observability, building end-to-end pipelines processing millions of records/day from sources like S3 and Kafka. Has hands-on experience with Airflow-based data quality automation, PySpark/Databricks transformations, and shipping versioned Python REST APIs deployed via Docker/Kubernetes with CI/CD (Jenkins) and monitoring (CloudWatch/Azure Logs).”

“LLM/MLOps engineer who has shipped production systems for complaint intelligence and contact-center NLU, including LoRA/RLHF-tuned LLaMA models deployed on GKE with vLLM and Vertex AI batch pipelines to BigQuery. Demonstrates strong practical focus on hallucination control, data imbalance mitigation, and production monitoring (Langfuse) with regression testing and canary rollouts, plus experience orchestrating complex workflows with AWS Step Functions.”

“Data/ML engineer with experience across pharma (Dr. Reddy Laboratories) and financial services (Cincinnati Financial, Capital One), building production NLP and entity-resolution systems that connect messy unstructured text with enterprise SQL data. Delivered semantic search with BERT + vector DB and domain fine-tuning (reported ~35% relevance lift), and builds robust pipelines using Airflow/dbt/Spark with strong validation, monitoring, and stakeholder-aligned rollout practices.”

Eric Low

Screened

Principal Engineering Leader specializing in platform, product, and AI advisory

14y exp

“Fractional CTO/lead engineer who shipped an end-to-end Next.js + FastAPI product experience (login, data processing results, chatbot Q&A) with an architecture designed to support future ML model integration. Has led large-scale engineering enablement (continuous delivery across ~150 devs/200 systems), owned production incident response with lasting test/contract improvements, and delivered a 3x productivity gain by fixing debugging/tooling bottlenecks while mentoring junior teams into independent delivery.”

“ML/AI engineer with hands-on ownership of both classical ML and GenAI systems in production. They built an end-to-end churn prediction service on AWS and also shipped RAG-based document search/summarization features, with clear experience in monitoring, hallucination reduction, cost/latency optimization, and creating shared Python/LLM infrastructure used across teams.”