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Vetted Databricks Professionals
Pre-screened and vetted.
Maurice Lange
Executive Technology & Data Leader specializing in cloud platforms, AI/ML, and enterprise data
“Former PwC Director with hands-on early-stage venture experience (e.g., BridgeLights, a big-data analytics concept for early fintech) spanning concept creation, platform architecture, and go-to-market experimentation. Strong focus on building scalable, modular data platforms with rigorous governance/compliance (data lineage, quality controls) and supporting technical diligence in investor-aligned environments.”
Jathin Shettigar
Intern Software Engineer specializing in edge AI deployment and distributed systems
“Full-stack engineer who built an enterprise search platform (Codlens) delivering natural-language Q&A over Jira/Slack using embeddings, vector DB search, re-ranking (RRF), and LLM responses with source grounding. Also designed and benchmarked a distributed IAM system with Postgres transaction-log replication and Raft-based quorum consistency, reporting ~253 TPS at ~60ms latency in a multi-node setup. Experience spans early-stage startups (Zetic AI, Sagwara Capital) and large-scale orgs (Akamai, Atlassian).”
Anvith Reddy Dodda
Mid-level AI Engineer specializing in GenAI, NLP, and MLOps
“LLM/agentic-systems engineer with PayPal experience hardening an LLM-powered fraud support assistant from prototype to production, focusing on low-latency distributed architecture, rigorous evaluation/testing, and security/compliance. Comfortable in customer-facing and GTM contexts—runs technical demos/workshops, builds tailored pilots, and aligns sales/CS with engineering to close deals and drive adoption.”
Mounika Gunturu
Senior Python Full-Stack Developer specializing in cloud-native microservices and data platforms
“Backend/data engineer from Oliver Wyman who built and ran production Python (FastAPI) services on AWS (ECS/Lambda/API Gateway) supporting risk modeling and regulatory reporting. Strong in reliability/observability, Glue-based ETL with data quality controls, and legacy SAS-to-Python modernization with rigorous parity validation; also demonstrated measurable SQL performance wins and cost-control improvements in serverless scaling. Based in Raleigh, NC and can travel onsite for important Bethesda-area meetings.”
Richard Bemis
Executive CIO/CTO/CDO specializing in data, AI/ML, and digital transformation
“Founder building a healthcare provider data management startup who has progressed from problem identification to product architecture, patent filing, prototype development, beta customer outreach, and angel fundraising. They also have experience performing technical assessments for VCs and approach company-building with a structured focus on customer demand, risk mitigation, IP protection, and candid core-team formation.”
Geetha Bommareddy
Mid-level AI/ML Engineer specializing in fraud detection and risk analytics in Financial Services
“At JP Morgan Chase, built and deployed a production LLM-powered RAG knowledge assistant to help fraud investigators and risk analysts quickly navigate regulatory updates and internal policies, reducing investigation delays and compliance risk. Strong focus on secure retrieval (RBAC filtering), reliability (layered testing + observability), and production constraints (latency/SLOs), with Airflow-orchestrated, auditable ML pipelines.”
Yash Pise
Mid-level Data Scientist specializing in Generative AI, LLMOps, and clinical data pipelines
“LLM/RAG engineer who has built and deployed corporate-scale systems at Novartis and Johnson & Johnson, including a healthcare AI agent that generates day-to-day treatment schedules. Recently handled a high-stakes safety incident (LLM suggesting overdose) by tightening model instructions and validating with ~200 test prompts, and has strong end-to-end data/embedding/vector DB pipeline experience (PySpark, FAISS, Pinecone) plus SME-in-the-loop evaluation (RLHF).”
Santhosh Kumar
Mid-level GenAI/ML Engineer specializing in LLM agents and RAG for Financial Services & Healthcare
“Built and deployed a production GenAI internal support agent at Bank of America (“Ask GPS/AskGPT”) using RAG on Azure, focused on reducing escalations and improving response quality for repetitive knowledge-based queries. Demonstrates strong production LLM engineering: custom LangChain orchestration, retrieval tuning to reduce hallucinations, rigorous offline/online evaluation, and model benchmarking with dynamic routing (e.g., GPT-4 vs Claude).”
Ashwin Ram
Junior Data Scientist specializing in Generative AI and applied machine learning
“At Evoke Tech, built a production LLM "Testbench" to quickly compare LLMs/embedding models and RAG strategies (semantic, hybrid BM25, re-ranking, HyDE, query expansion) to select optimal architectures for different client needs. Also developed a multi-agent, multimodal (voice/text) RAG system for live catalog retrieval and safe product recommendations using LangGraph/LangChain with LangSmith monitoring, and regularly translated PM/UX goals into concrete agent behaviors via demos and flowcharts.”
Utkarsh Joshi
Senior Data Scientist specializing in ML, NLP, and GenAI analytics
“Built and deployed an LLM-powered analytics assistant enabling business users to ask questions in plain English and receive validated Spark SQL executed in Databricks, with a Streamlit/Flask UI. Addressed strict client schema-privacy constraints by implementing a RAG strategy and ultimately leveraging AWS Bedrock and fine-tuned reference docs. Also has production ML pipeline experience using Docker + Airflow and AWS (S3/ECS/EC2) for financial classification models.”
“GenAI/data engineering practitioner with production experience across Equinix, Optum, and Citibank—built an Azure OpenAI (GPT-4) + LangChain document intelligence platform processing 1.5M+ docs/month and a HIPAA-compliant Airflow healthcare pipeline handling 5M+ claims/day. Also delivered a real-time fraud detection + explainability system using LightGBM and a fine-tuned T5 NLG component, improving fraud accuracy by 15%+ while partnering closely with compliance stakeholders.”
Edin Samuel Joselyn Chandrakumar
Senior Engineering Manager specializing in cloud platforms and risk systems
“Engineering leader who proposed and delivered a new API-based document management platform to replace a vendor-dependent system, improving latency by ~1s and availability to 99.9% while migrating legacy data. Also drove Python-based automation of ~12 workflows via third-party API integrations and led an SSO/auth integration focused on backward compatibility and high login success rates.”
Uday Chilakala
Mid-level Machine Learning Engineer specializing in NLP, computer vision, and RAG systems
“Machine learning/NLP engineer who built a production-oriented retrieval-based AI system at Morgan Stanley for healthcare use cases, combining RAG over unstructured patient records with deep-learning medical image segmentation (U-Net/Mask R-CNN). Strong in end-to-end pipelines and MLOps (Spark/MongoDB, AWS SageMaker, CI/CD, monitoring, automated retraining) and in entity resolution/data quality validation for noisy clinical data.”
Ethan Lam
Junior Software Engineer specializing in data platforms and full-stack development
“Software engineer with Warner Music Group experience owning and shipping analyst-facing data products (marketing/streaming data dashboards) end-to-end with high adoption through continuous stakeholder feedback. Also builds side projects with TypeScript/React and domain-driven API design, emphasizing flexibility (including swapping databases mid-development) and pragmatic microservices reliability patterns (logging, timeouts, retry backoff).”
Sai Gowtham Madaka
Mid-level Data Engineer specializing in streaming and cloud data platforms for financial services
“Data engineering-focused candidate (internship/project experience) who built end-to-end pipelines processing a few million transactional records/day for fraud detection and reporting, using Airflow, Python/SQL, and PySpark with strong emphasis on data quality gates, idempotency, and monitoring. Also implemented an external web/API data collection system with anti-bot tactics and schema-change quarantine, and shipped a versioned Flask API to serve curated warehouse data.”
Pooja Dokuri
Mid-level AI/ML Engineer specializing in GenAI, RAG pipelines, and cloud MLOps
“Built and deployed a production LLM + vector search clinical decision support system at UnitedHealth Group, retrieving medical evidence and patient context in real time for prior authorization and risk scoring. Strong in end-to-end RAG architecture (Hugging Face embeddings, Pinecone/FAISS, SageMaker, Redis) plus orchestration (Airflow/Kubeflow) and rigorous evaluation/monitoring, with demonstrated ability to align solutions with clinical operations stakeholders.”
Supreetha Kashyap
Mid-Level Software Engineer & Data Analyst specializing in cloud analytics and BI
“Built and owned an end-to-end Seat Allocation & Management System at Accenture, replacing a legacy process with a scalable web app used across teams. Deep focus on reliability under concurrency (transactions + unique constraints + idempotent APIs) and on Postgres performance tuning (composite indexes, EXPLAIN ANALYZE), plus post-launch production support and monitoring.”
Prasanna Chelliboyina
Mid-level Machine Learning Engineer specializing in forecasting, NLP, and GenAI
“GenAI/ML engineer with production experience building multilingual LLM systems (English/Spanish) and RAG-based clinical documentation summarization at Walgreens, combining prompt engineering, structured output validation, and rigorous evaluation (ROUGE + pharmacist review). Also orchestrated end-to-end ML pipelines for demand forecasting using Apache Airflow, PySpark, and MLflow with scheduled retraining and production monitoring.”
John Hoffman
Senior Data Engineer specializing in Databricks, Spark, and AWS for government healthcare data systems
“Python/AWS engineer focused on batch-processing and data workflows, including building reusable S3/boto3 utilities with reliability features and IAM-based auth. Has led low-risk legacy modernizations using parity testing plus a month of parallel production runs, and has owned production issues end-to-end (including fixing a client-side Excel macro) while contributing to significant AWS cost reductions (~$10k/month).”
Dhyey Desai
Intern AI/ML Engineer specializing in RAG, multimodal AI, and LLM systems
“Built and shipped 'PetPulse,' a production AI pet-health note system that records voice notes, transcribes them, converts transcripts into structured symptom/event data, and supports grounded Q&A over a user’s notes and vet PDFs. Demonstrates full-stack LLM product execution (FastAPI + GPT-4 + Firebase), with concrete reliability/performance work (async endpoints, caching, RAG/embeddings, function calling) and user-centered iteration with a non-technical product stakeholder.”
Anirban Ghosh
Mid-level Machine Learning Engineer specializing in data science and cloud systems
“ML engineer who independently pitched and built a recommendation engine at Danske Bank in a legacy fintech environment, creating compliant data pipelines and deployment infrastructure from scratch and delivering a 62% engagement lift with 70%+ advisor adoption. Also worked at AWS on classification and GenAI-powered reporting systems, with strengths spanning production ML, platform setup, monitoring, and research-to-production optimization.”
Chaitanya Prasad Reddy Narala
Mid-level AI/ML Engineer specializing in FinTech risk and fraud systems
“Senior AI/ML engineer focused on production LLM systems, combining RAG, fine-tuning, distributed training, and AI safety to ship scalable real-time moderation and conversational AI platforms. Stands out for pairing deep AWS/Kubernetes MLOps expertise with measurable impact: 40% lower latency/cost, 30-50% fewer hallucinations, and major reliability gains through observability and automation.”
Mohammad Gouse Ali Shaik
Mid-level Software Development Engineer specializing in cloud-native AI/ML systems
“AI/ML-focused engineer with practical experience building RAG-based and multi-agent systems, including architectures for retrieval, reasoning, context processing, and response generation. Stands out for combining LLM productivity gains with disciplined software engineering practices like validation, monitoring, and reproducibility.”
Shruti Gaikwad
Mid-Level Software Engineer specializing in secure cloud microservices and FinTech
“Built and owned major parts of a real-time distributed AI fraud-detection pipeline (ingestion, inference microservice integration, and automated action layer), optimizing latency and observability and reducing false positives by ~35%. Understands ROS/ROS2 concepts (nodes/topics/services) and planned hands-on ramp-up via ROS2 pub/sub exercises and Gazebo simulation, but has not worked on physical robots or ROS in production.”