Vetted Azure DevOps Professionals

“AI/ML practitioner with Northern Trust experience who has shipped production LLM systems (internal support assistant) using RAG, vector databases, orchestration (LangChain/custom pipelines), and rigorous monitoring/feedback loops. Also built AI-driven fraud detection/risk monitoring solutions in a regulated financial environment, emphasizing explainability (SHAP), audit readiness, and stakeholder trust through dashboards and clear communication.”

“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).”

“Software engineer with deep experience in real-time payments and event-driven microservices. Built a React/TypeScript + Spring Boot system using RabbitMQ, and created an internal operations dashboard that improved visibility into message-processing workflows for engineering, support, and SRE. Strong in experimentation-driven product iteration (feature flags/A-B tests) and in scaling reliability via idempotent consumers and end-to-end observability.”

“Built and productionized an NLP-powered customer support assistant at JPMorgan Chase for digital banking, focused on reducing response time for repetitive client queries. Strong in real-world AI deployment challenges—sensitive data handling, low-latency FastAPI services, and AWS/Kubernetes operations with CI/CD—plus a metrics- and guardrails-driven approach to reliable AI workflows.”

“AI/ML engineer with HP experience building and productionizing an LLM-powered document intelligence platform (LangChain + Pinecone) to deliver semantic search and contextual Q&A across millions of enterprise support documents. Demonstrates strong MLOps and scaling expertise (Airflow, Kubernetes autoscaling, Triton GPU inference, monitoring with Prometheus/W&B) plus a structured approach to evaluation (A/B tests, shadow deployments, failover) and effective collaboration with non-technical stakeholders.”

“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.”

“Robotics/ADAS engineer who built an assistive feeding robot with reliable 3D mouth tracking (RealSense + MediaPipe) and ROS 2 integration to a WidowX250s arm, solving depth-noise, timing, and workspace/singularity issues for stable low-latency behavior. Also optimized a real-time lane-keeping controller at Hyundai using signal logging/replay, filtering (LPF/Kalman), and feedforward+PI tuning, with experience across SIL/HIL and CAN-based ECU integration.”

“Built and deployed a secure, production LLM-based document summarization and risk-highlighting tool for financial auditors, running inside a private Azure environment to protect confidential data. Focused on reliability (hallucination mitigation via retrieval-based prompts and source citations) and validated performance through comparisons to auditor summaries plus a user pilot, cutting review time by about half.”

“Backend/data engineer with experience at Citi and Elevance Health, building end-to-end pipelines and data services in regulated, high-volume environments. They combine Python, SQL, .NET, Azure Functions, and strong observability/reliability patterns to improve processing speed, reduce manual effort, and maintain high uptime across financial and healthcare data platforms.”

Anirban Ghosh

Screened

Mid-level Machine Learning Engineer specializing in data science and cloud systems

Seattle, WA4y exp

“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.”

Edward Diehl

Screened

Senior Technical Project Manager specializing in data, systems, and FinTech delivery

Morristown, NJ12y exp

“Enterprise implementation and modernization leader with 10+ years across JP Morgan, StoneX, and Majesco, delivering complex B2B/SaaS programs in highly regulated insurance and financial services environments. Has progressed from project coordination into full program ownership, managing $5M+ initiatives and portfolios of 4-6 concurrent enterprise implementations with executive-facing governance.”

Deepthi Pamisetty

Screened

Mid-level Full-Stack Engineer specializing in FinTech and cloud-native systems

Dallas, TX6y exp

“Full-stack engineer with production experience building AI-powered search and automation systems at JPMorgan Chase and customer-facing product features at Wayfair. Stands out for combining React frontend work with backend microservices, RAG/LangChain AI integration, and cloud-scale performance tuning, including a support chatbot that reduced ticket resolution time by 35%.”

“Backend engineer with hands-on experience scaling a Python/Flask incident-logging platform processing thousands of daily logs. Strong in performance tuning (PostgreSQL/SQLAlchemy query optimization, partitioning, summary tables) and reliability patterns (Redis caching, Celery background workers, Docker + Jenkins CI/CD), with some multi-tenant data isolation experience via separate DBs/schemas.”

“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.”

“Built and deployed a production LLM/RAG knowledge assistant integrating internal docs, wikis, and ticket histories to reduce tribal-knowledge dependency and repetitive questions. Emphasizes reliability via grounding + a validation layer, and achieved major latency gains (>50%) through vector index optimization, caching, quantization, and selective re-validation. Comfortable orchestrating end-to-end LLM/data workflows with Airflow, Prefect, and Dagster, including monitoring and alerting.”

“Talent Acquisition/Recruiting Operations leader with 10+ years driving large-scale K-12 district hiring, including staffing across 200 schools and managing thousands of candidates annually. Known for redesigning fragmented recruiting workflows and implementing ATS solutions (Lever, AppliTrack, Taleo, Oracle HCM) to deliver measurable gains: 15% vacancy reduction, 20% faster time-to-hire, 30% fewer compliance errors, and 25% faster onboarding.”

“Platform-focused engineer experienced in productionizing ML/LLM systems: containerized a local prototype, implemented CI/CD, deployed to Kubernetes with scaling controls, and added monitoring/logging. Comfortable diagnosing real-time issues in LLM/agent workflows using logs/metrics and incident stabilization tactics, and supports sales calls by clearly explaining production scalability to unblock customer decisions.”

“LLM/RAG engineer who has shipped production systems in high-stakes domains (fraud analytics at Mastercard and security compliance as a CI/CD gate). Strong focus on reliability: hybrid retrieval for latency, citation-backed outputs for trust, and code-driven eval/regression pipelines using golden datasets. Also built scalable OCR-based ingestion for messy classroom artifacts (handwriting, PDFs, whiteboard photos) using Go/Python and cloud services.”

“Infrastructure reliability/capacity-focused engineer with hands-on IBM Power/AIX (LPAR/DLPAR, HMC, VIOS) performance troubleshooting and modern cloud-native delivery experience. Built production CI/CD and Terraform-managed AWS/EKS environments, and has led real incident recoveries spanning Kubernetes autoscaling and AWS quota constraints with concrete RCA and prevention improvements.”

Daniel Panrong Dai

Screened

Junior Software Engineer specializing in AI and full-stack development

New York, NY1y exp

“Consulting-background AI practitioner who led a production LLM pipeline on Snowflake Cortex to map hundreds of thousands of messy OCR/form-based contract fields into standardized Salesforce fields, including confidence scoring and an LLM-driven feedback loop. Strong focus on real-world constraints—token limits, cost control, and evaluation without ground truth—paired with frequent stakeholder-facing progress reporting.”

Akshaya Chiduruppa

Screened

Mid-level Quality Assurance Engineer specializing in AI/ML and Apple ecosystem testing

Seattle, WA4y exp

“QA automation engineer with end-to-end ownership of a regression suite for a warehouse loan management platform (.NET/Angular), using Selenium (Java/Cucumber/POM) and Cypress. Improved suite stability and expanded risk-based coverage (DB/API/SQL, RBAC approval workflows), catching critical financial defects like EMI calculation errors and cutting regression effort by ~50% while gating releases via GitLab CI/CD with actionable Slack reporting.”

Jhansi Bendi

Screened

Senior Software Engineer specializing in cloud-native microservices and event-driven systems

Antioch, CA18y exp

“Senior engineer/tech lead with 18+ years building large-scale distributed applications, specializing in performance and reliability improvements. Recently owned multiple apps on an email personalization team, shipping major optimizations (including a push-update feature and audience-count architecture redesign) that reportedly lifted system performance from ~50% to ~99% while also leading code standards, reviews, and mentoring.”

Sanjana Duvva

Screened

Mid-level AI/ML Engineer specializing in Generative AI, LLMOps, and MLOps

5y exp

“Built and deployed an AWS-based LLM/RAG ticket triage and knowledge retrieval system (Pinecone/FAISS + Step Functions + MLflow) that cut support resolution time by 20%. Demonstrates strong production focus on hallucination reduction, PII security, and low-latency orchestration, with measurable evaluation improvements (e.g., ~25% grounding accuracy gain via re-ranking) and proven collaboration with support operations stakeholders.”

“5-year .NET full stack developer who has applied AI-assisted development in an enterprise Cisco environment, using tools like GitHub Copilot and ChatGPT to accelerate microservice API delivery while maintaining architecture, security, and code quality standards. Notably reports a roughly 30% reduction in development time on a customer policy management/claims processing project through disciplined use of AI for boilerplate, testing, and design validation.”