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Vetted ETL Pipelines Professionals
Pre-screened and vetted.
Mid-level AI/ML Engineer specializing in MLOps, real-time ML, and LLM/RAG systems
Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Mid-level AI & ML Engineer specializing in NLP, LLMs, and scalable ML systems
Mid-level AI/ML Engineer specializing in LLM evaluation, RAG, and GPU-accelerated inference
Mid-level Full-Stack Developer specializing in AI and FinTech platforms
Senior Software Engineer specializing in Python, AI/ML, and AWS cloud-native systems
Mid-level AI/ML Engineer specializing in multimodal and generative AI at scale
Mid-level Machine Learning Engineer specializing in MLOps and scalable ML pipelines
Mid-level Full-Stack Python Developer specializing in cloud-native FinTech and GenAI
Intern Full-Stack Software Engineer specializing in scalable web platforms
Mid-level Python Backend Developer specializing in FinTech and ML-driven fraud detection
Mid-level AI/ML Engineer specializing in recommendation, retrieval, and MLOps
Mid-level Software Engineer specializing in backend systems and FinTech
Sharath Nyalakonda
Mid-level AI/ML Engineer specializing in NLP, graph models, and MLOps for FinTech and Healthcare
“AI/ML engineer who has deployed production LLM/transformer-based systems for merchant intelligence and fraud/support optimization, delivering +27% merchant engagement and +18% payment success. Deep experience in privacy-preserving, PCI DSS-compliant data/ML pipelines (Airflow, AWS Glue, Spark, Delta Lake) and scalable microservices on Kubernetes, plus proven cross-functional delivery in healthcare claims analytics at UnitedHealth Group (12% HEDIS claim reduction).”
Nehal Mahankali
Mid-level Full-Stack Python Developer specializing in cloud-native banking applications
“Backend engineer who built a low-latency real-time transaction API in Python/Flask, with strong depth in PostgreSQL/SQLAlchemy performance tuning (time-based partitioning, indexing, connection pooling). Has production experience integrating ML scoring and OpenAI-style APIs with safety/latency controls, and designing multi-tenant isolation strategies including per-tenant pooling/caching and premium-tenant isolation.”
Poorna Pedapudi
Mid-Level Software Engineer specializing in distributed backend systems and cloud-native microservices
“Software engineer focused on data platforms and applied LLM systems: built an internal data quality monitoring layer to catch silent data drift and iterated post-launch after finding ~30% false-positive alerts, reducing noise via dynamic baselines and improved structured logging. Also shipped a production RAG-based internal knowledge assistant over Jira/Confluence with citations, confidence-based fallbacks, and nightly automated evals to prevent regressions.”
Jiacheng Yin
Intern software engineer specializing in AI, backend systems, and cloud infrastructure
“Backend/AI systems engineer who has shipped production LLM agents focused on prompt engineering, code generation, and incident-response automation. Stands out for combining strong agent orchestration and reliability engineering with measurable business impact, including 60-70% cost reductions, 45% lower monthly LLM spend, and a 5x increase in developer iteration speed.”
Joseph Rivas
Senior AI/ML Engineer specializing in GenAI, MLOps, and computer vision
“ML/AI engineer with hands-on ownership of production document intelligence and GenAI systems, spanning model experimentation, AWS deployment, monitoring, and iterative optimization. Stands out for turning document-heavy workflows into reliable, near real-time products with measurable gains in accuracy, latency, and manual-effort reduction, while also shipping citation-grounded RAG features that drove user trust and adoption.”
Kaustubh Rai
Junior Software Engineer specializing in scalable distributed systems and cloud platforms
“Backend engineer with experience at UnitedHealth Group redesigning a high-traffic Spring Boot microservice from blocking to reactive architecture during peak season, cutting median latency by 47% for a service used by ~10M customers annually. Strong in Kubernetes-based deployment/scaling and pragmatic rollout strategies (blue-green/incremental traffic shifting) with performance and database troubleshooting.”
Jiacheng Yin
Intern Software Engineer specializing in data engineering and AI agent systems
“AI engineer at Anote.ai who built and shipped a production multi-agent LangGraph/LangChain/Ray RAG platform for enterprise search and workflow automation, supporting 3 commercial products and 100+ developers. Drove measurable gains (30% accuracy improvement, lower latency) and improved reliability with Redis-based state checkpointing, message-queue synchronization, and Milvus retrieval optimizations, while partnering with PMs/clients to add transparency features like confidence scores and real-time logs.”
Pranav Puranik
Senior AI Engineer specializing in LLMs, RAG, and multimodal NLP
“Built a production LLM/RAG assistant for insurance/health claims agents that ingests 100–200 page patient PDFs via OCR (migrated from local Tesseract to Azure Document Intelligence) and delivers grounded claim detail retrieval plus summaries with PII/PHI guardrails. Experienced orchestrating large workflows with Celery worker pipelines and AWS Step Functions (S3-triggered, Fargate-based batch inference/accuracy aggregation), and collaborates closely with non-technical SMEs (claims agents/nurses) through shadowing, iterative demos, and SME-defined evaluation.”