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Vetted ETL Professionals
Pre-screened and vetted.
Principal Data Engineer specializing in cloud-native AI and data platforms
Intern Software Engineer specializing in AI/ML and LLM retrieval systems
Senior Full-Stack Engineer specializing in AI/ML, LLMs, and RAG systems
Mid-Level Backend Software Engineer specializing in FinTech and cloud-native platforms
Mid-level Data Engineer specializing in cloud-native big data pipelines and analytics
Senior Machine Learning Engineer specializing in LLMs and Generative AI
Senior Software Engineer specializing in full-stack platforms, MLOps, and LLM search
Mid-Level Full-Stack Software Developer specializing in AWS cloud and automation
Senior Data Engineer specializing in cloud data platforms and real-time analytics
Senior Data Engineer specializing in cloud data platforms and large-scale ETL
Mid-level Data Engineer specializing in big data platforms and analytics infrastructure
Senior Backend Software Engineer specializing in AWS serverless and data pipelines
Senior Backend Engineer specializing in Python and cloud data platforms
Director-level Engineering Leader specializing in SaaS platforms, cloud architecture, and SRE
Rhutwij Tulankar
Engineering Manager and ML/Data Architect specializing in scalable data platforms and personalization
“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.”
Kaustubh Rai
Mid-level Software Engineer specializing in distributed systems and cloud-native microservices
“Software engineer with ~2 years at UnitedHealth Group plus CMU coursework/TA experience, spanning backend modernization and cloud-native operations. Worked on migrating a customized open-source EDI system from Python 2 to Python 3 while improving SQLite database traceability via JSON exports, and has hands-on Kubernetes microservices deployment on Azure using Helm, HPA, and Jenkins-based Git-triggered CI/CD. Also built a large-scale real-time ride-hailing simulation using Kafka + Samza with explicit fault-tolerance strategies.”
Andrew Fares
Mid-level Data Engineer specializing in AI, GenAI, and cloud data platforms
“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.”
David Proctor
Executive Enterprise Architecture & AI Strategy Leader specializing in modernization and agentic AI platforms
“Technically and operationally oriented builder with startup ideas of their own, including an AI services firm in the ideation stage. Stands out for a practical understanding of venture studios and accelerators, with fluency in founder-market fit, MVP validation, hiring, and investor readiness, and a measured approach to going all-in only on high-signal opportunities.”
Ali Rezafard
Director of Engineering specializing in cybersecurity SaaS platforms and cloud-scale backend systems
“Director of Engineering at Proofpoint for 8 years, leading architecture and integration of Java microservices within a detection platform. Demonstrates pragmatic delivery leadership—incurring short-term cost to meet launch deadlines, then systematically paying down technical debt and optimizing AWS spend—plus a disciplined, long-horizon approach to backward-compatible API/schema evolution across many dependent services.”
Gowri Kajipuram
Mid-level AI/ML Engineer specializing in LLMs, RAG, and multimodal deep learning
“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.”