Vetted MLOps Professionals

Rohan Varma Bandari

Screened

Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG

USA4y exp

“Built production LLM + hybrid RAG and multi-agent orchestration systems at Wells Fargo to automate complaint document/audio transcript understanding and categorization, addressing vocabulary drift via embedding + vector index updates instead of frequent retraining. Strong in LLM workflow reliability (testing/benchmarks/observability) and stakeholder-facing delivery with explainability (citations/SHAP-style justifications) and Tableau dashboards.”

Muaaz Syed

Screened

Mid-level AI/ML Engineer specializing in NLP and conversational AI

Richardson, TX4y exp

“ML/NLP engineer focused on real-time IT ops analytics, building a predictive maintenance/anomaly detection platform end-to-end (multi-source ETL, streaming, modeling, and production deployment on GCP/Vertex AI). Uses deep learning (LSTMs, autoencoders/VAEs) plus embeddings (SentenceBERT) and vector search to improve incident correlation and search, citing ~40% reduction in duplicate alert noise.”

Shivani Sharma

Screened

Mid-Level Software Engineer specializing in Cloud, DevOps, and MLOps

Boston, MA3y exp

“Built and productionized a recommendation system from notebook prototype into a low-latency, scalable Cloud Run service using Docker, FastAPI, Terraform, CI/CD (GitHub Actions), and MLOps tooling (Vertex AI, MLflow). Experienced diagnosing real-time workflow issues using structured logging/ELK and GCP metrics, including resolving intermittent 504s by fixing unbounded SQL and adding caching. Also partners with sales/customer teams (Wasabi) to deliver tailored demos, troubleshoot, and drive onboarding/adoption.”

“Senior data scientist with ~5 years’ experience building production ML/NLP systems in finance (Wells Fargo) and deep learning for sensor analytics in connected vehicles (Medtronic). Has delivered end-to-end platforms combining time-series forecasting with transformer-based NLP, including automated drift monitoring/retraining (MLflow + Airflow) and standardized Docker/CI/CD deployments; achieved a reported 22% precision improvement after domain fine-tuning.”

Hemanth Kumar Gajagiri

Screened

Mid-level Full-Stack AI Engineer specializing in agentic systems and scalable platforms

San Francisco, CA6y exp

“AI-focused full-stack/DevOps engineer who goes beyond using copilots and has built production-oriented LLM systems such as natural-language-to-SQL and structured insight extraction pipelines. Stands out for treating AI as an accelerator rather than a replacement, with a strong emphasis on guardrails, validation, observability, and safe deployment practices in agent-based and distributed systems.”

“Founding member of Tausight, building AI systems to detect and protect PHI for healthcare organizations; helped take the company through post–Series A funding and exited after ~6 years. Drove a strategic collaboration with Intel’s OpenVINO team—becoming the first to deploy it in a real production system and improving model performance by ~30% on customer Intel-CPU machines.”

“Built and shipped a production RAG-based enterprise knowledge assistant to replace slow/inaccurate search across millions of documents, using LangChain orchestration with GPT-4/LLaMA and vector databases. Strong focus on production constraints—latency, hallucination control, and cost—using hybrid retrieval, guardrails, LLM-as-judge validation, and model routing, and has experience translating non-technical stakeholder pain points into measurable outcomes.”

“Built and productionized an enterprise retrieval-augmented generation platform for internal knowledge over large unstructured corpora, emphasizing trust via strict citation/grounding and hybrid retrieval (BM25 + FAISS + cross-encoder re-ranking). Demonstrates strong scaling and cost/latency optimization through incremental indexing/embedding and index partitioning, plus disciplined evaluation/observability practices. Has experience operationalizing pipelines with Airflow/Databricks/GitHub Actions and partnering closely with risk & compliance stakeholders on auditability requirements.”

“BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.”

“Built and led deployment of an autonomous, self-correcting multi-agent knowledge retrieval and validation system at HCA Healthcare to reduce heavy manual research/validation in clinical/compliance documentation. Deeply focused on production reliability and cost—used LangGraph StateGraph orchestration plus ONNX/CUDA/quantization to cut GPU costs by 25%, and partnered with the Compliance VP using real-time contradiction-rate dashboards to hit a 40% automation goal without compromising compliance.”

“AI/LLM engineer with production experience in legal tech: built a GPT-4 + LangChain RAG summarization system at Govpanel that reduced legal case-file review time by 50%+. Previously at LexisNexis, orchestrated end-to-end Airflow data/AI pipelines processing 5M+ legal documents daily, improving ETL runtime by 35% with robust validation, monitoring, and SLAs.”

“Built and shipped production generative AI/RAG assistants in regulated financial contexts (S&P Global), automating compliance-oriented Q&A over earnings reports/filings with grounded answers and citations. Experienced across the full stack—AWS-based ingestion (PySpark/Glue), vector retrieval + LangChain agents, GPT-4/Claude model selection, and production reliability (monitoring, caching, retries) plus rigorous evaluation and regression testing.”

“Full-stack/backend engineer with deep insurance claims domain experience who built and operated a microservices + ETL platform (Java/Spring Boot + Python + Kafka/Databricks) processing 1M+ daily transactions. Combines production-grade reliability (99.7% uptime, zero-downtime blue/green releases, strong observability) with customer-facing UI delivery (AngularJS/React+TS dashboards and a hackathon-winning research chatbot).”

Somil Shah

Screened

Mid-level AI/ML Engineer specializing in generative AI, RAG platforms, and LLM agents

San Francisco, CA4y exp

“AI/LLM engineer who has shipped 10+ production applications, including InvestIQ on GCP—a production-grade RAG due-diligence engine that ethically scrapes web/PDF sources, builds a ChromaDB knowledge base, and delivers analyst-style dashboards plus a citation-backed chat copilot. Deep focus on reliability (evidence-only answers, hard citations, refusal gating), retrieval tuning, and orchestration (Airflow/Cloud Composer), plus multi-agent systems (CrewAI with 7 specialized finance agents).”

Bala Venkateswarlu K

Screened

Mid-level Data Scientist specializing in Generative AI, NLP, and MLOps

USA5y exp

“Built and deployed an LLM-powered claims-document summarization system (insurance domain) that cut agent review time from 4–5 minutes to under 2 minutes and saved 1,200+ hours per quarter. Hands-on across orchestration and production infrastructure (Airflow retraining DAGs, Kubernetes, SageMaker endpoints, FastAPI) and recent RAG workflows using n8n + Pinecone, with a strong focus on reliability, cost, and explainability for non-technical stakeholders.”

“Built a production, real-time clinical documentation system at HCA that converts doctor–patient conversations into structured clinical summaries using speech-to-text, LLM summarization, and RAG. Demonstrated measurable gains from medical-domain fine-tuning (clinical concept recall +18%, ROUGE-L 0.62 to 0.74) while meeting HIPAA constraints via PHI anonymization and encryption, and deployed via Docker/FastAPI with CI/CD and monitoring.”

“GenAI/ML engineer with hands-on experience building production financial intelligence and document summarization systems at Citibank. Stands out for combining LLM fine-tuning, hybrid RAG, multi-agent workflows, and strong MLOps/observability practices to deliver measurable business impact, including 60% faster analyst retrieval, 31% higher precision, and 99%+ uptime.”

“ML/AI engineer with hands-on experience building production systems in financial services, including a real-time underwriting analytics platform at Hartford Financial Services. Stands out for combining classic ML, low-latency API deployment, monitoring, and emerging LLM/RAG design patterns, with measurable impact including 20% better decision accuracy, sub-200ms latency, and 5M+ records processed daily.”

“Software engineer currently building AI-powered backend systems for interview analysis, with end-to-end ownership of an LLM-based monitoring platform. Stands out for combining practical product delivery in an ambiguous early-stage environment with measurable impact: over 40% reduction in manual review effort and roughly 20% lower inference cost.”

“Backend/cloud engineer who built an AWS serverless IoT system that computes Bluetooth beacon locations from telemetry using heavy scientific Python (NumPy/SciPy/pandas) packaged as Dockerized Lambda, integrated with Java microservices and scheduled batch orchestration. Has deep AWS delivery experience (CI/CD with Code* tools, CloudFormation, cost controls) and has led high-severity incident response including CloudTrail forensics and infrastructure recovery after a compromised-keys crypto-mining attack.”

“Engineering/product leader who builds business-aligned technology roadmaps and scales pod-based orgs with strong delivery discipline (OKRs, CI/CD, QA automation). Led a SaaS supply-chain application adopted by Fortune 100 customers, citing ~$4M MRR and ~87% gross profit, and has hands-on experience standardizing LLM + cloud/MLOps architectures with security/compliance guardrails. Also created the PISEK methodology and used it to run distributed innovation sprints (e.g., an AI ETA predictor moved from pilot to production).”

“Telecom (Verizon) AI/ML practitioner who built a production multimodal system that ingests messy customer issue reports (calls, chats, emails, screenshots, videos) and turns them into confidence-scored incident summaries with reproducible steps and evidence links. Also built KPI/alarm-to-ticket correlation to rank likely root-cause domains (RAN/Core/Transport), cutting triage from hours to minutes and improving MTTR.”

“AI/ML engineer with forensic analytics and healthcare claims experience (Optum), building production LLM/RAG systems to surface context-driven fraud patterns from unstructured claim notes and explain risk to investigators. Strong in large-scale retrieval performance tuning, legacy API integration with reliability patterns (SQS, circuit breakers), and MLOps orchestration on Airflow/Kubernetes with rigorous testing, monitoring, and stakeholder-friendly interpretability.”