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Vetted ETL Pipelines Professionals
Pre-screened and vetted.
Senior Data Scientist specializing in large-scale ML systems and recommendations
Mid-level AI/ML Engineer specializing in RAG, NLP, and MLOps
Mid AI/ML Engineer specializing in LLMs, RAG, and multimodal systems
Senior Machine Learning Scientist specializing in LLMs, RAG, and health AI
Kusumita Dasgupta
Director-level Engineering Leader specializing in data platforms, cloud systems, and LLM products
“Engineering leader/player-coach with recent hands-on work delivering an agentic AI MVP on Amazon Bedrock (conversational UI + supervisor agent routing between internal knowledge and external sources). Previously drove large-scale data platform cost optimization at Twitter, saving ~$3M–$5M annually, and has owned production incidents end-to-end with a focus on analytics/monitoring improvements and team coaching.”
Pavithra Kollu
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and search systems
“Backend/ML infrastructure engineer with experience at Perplexity and Meta building production evaluation, monitoring, and retrieval systems for AI search, autonomous agents, and LLM-powered workflows. Particularly strong in turning messy manual quality-review processes into reusable Python/FastAPI automation with measurable impact, including major gains in search relevance, latency, and grounded answer quality.”
Vinay Ramrupe
Mid-level AI/ML Engineer specializing in LLM and enterprise generative AI
“ML/AI engineer focused on taking LLM systems from experimentation to reliable production, including enterprise copilot and RAG-based knowledge retrieval use cases. Stands out for combining data pipelines, model training, inference optimization, automated evaluation, and safety guardrails, with cited impact including 20% throughput gains and 30% less manual evaluation effort.”
Senior Full-Stack Engineer specializing in scalable web platforms and AI-driven products
Senior Full-Stack Software Engineer specializing in cloud-native microservices and AI platforms
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and scalable GPU inference
Mid-level Machine Learning Engineer specializing in search, retrieval, and generative AI
Senior Software Engineer specializing in healthcare imaging and FinTech systems
Mid-level Software Engineer specializing in Python, distributed systems, and AI backend services
Mid-level AI/ML Engineer specializing in LLM training, RAG, and low-latency inference
Senior Software Engineer specializing in AI backend platforms and FinTech systems
Kenil Tanna
Staff-level Machine Learning Engineer specializing in LLMs and MLOps for Financial Services
“Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).”
Kowshika M
Mid-level AI/ML Engineer specializing in LLM fine-tuning, inference optimization, and AI safety
“AI/LLM engineer with production experience at NVIDIA, where they fine-tuned and deployed a financial-services chatbot and cut latency ~50% using TensorRT + NVIDIA Triton, scaling via Docker/Kubernetes. Also has consulting experience at Accenture delivering a predictive maintenance solution for a logistics network, bridging non-technical stakeholders with actionable dashboards.”
Dilpreet Singh
Executive CTO and Founder specializing in AI platforms and hyper-scale SaaS
“CTO-minded builder seeking to join a startup; previously created an AI-driven platform that abstracted away DevOps and infrastructure for drug discovery researchers. Emphasizes high-leverage, zero-to-one execution with managed cloud/open-source tooling, and a strong reliability/reproducibility mindset validated against existing scientific pipelines.”
Shuvam Mitra
Mid-level Data Scientist specializing in anomaly detection and production ML
“Interned at Backblaze building production AI systems for incident response and security operations, including an internal LLM-powered incident triage assistant that used Snowflake + RAG over historical tickets/postmortems and delivered results via Slack and a web UI. Emphasizes reliability (PII filtering, grounding, schema validation, fallbacks) and rigorous evaluation/observability (offline replay, partial rollouts, time-to-first-action metrics, Prometheus/Grafana).”