Vetted Azure Kubernetes Service Professionals

Pre-screened and vetted.

AS

Senior Software Engineer specializing in hybrid cloud, Kubernetes, and MLOps

Beaverton, OR12y exp
NikeUniversity of Texas at Austin
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BS

Senior Machine Learning Engineer specializing in computer vision and healthcare AI

Chicago, IL16y exp
ServiceNowNortheastern Illinois University
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JT

Staff Machine Learning Engineer specializing in MLOps, cloud AI, and generative AI

Vineland, NJ11y exp
SlalomCity University of New York
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SR

Mid-level Software Engineer specializing in distributed systems and full-stack web applications

Seattle, WA6y exp
AmazonUniversity of Alabama at Birmingham
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PS

Senior Software Engineer specializing in cloud-native data platforms

6y exp
EParts ServicesCarnegie Mellon University
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SG

Mid-level Full-Stack Developer specializing in AI-driven FinTech platforms

Remote, USA4y exp
KPMGWichita State University
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ZJ

Senior Software Engineer specializing in Healthcare IT and cloud-native microservices

Wesley Chapel, FL11y exp
OptumGeorgia Tech
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MM

Senior DevSecOps & Observability Engineer specializing in cloud automation and STIG compliance

Orlando, FL7y exp
IBMMercer University
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BC

Mid-level DevOps Engineer specializing in cloud-native CI/CD and Kubernetes

Arlington, VA5y exp
CoforgeWilmington University
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SK

Mid-level Full-Stack Software Engineer specializing in cloud-native and AI-driven applications

6y exp
Fidelity InvestmentsUniversity of Texas at Dallas
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ST

Senior Site Reliability Engineer specializing in multi-cloud, Kubernetes, and observability

Stamford, CT9y exp
Vanguard
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TS

Mid-level Platform Engineer specializing in cloud infrastructure and DevOps

New York, NY6y exp
Atlassian
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CM

Executive Engineering Leader specializing in cloud, DevSecOps, and large-scale platform modernization

Tampa, FL17y exp
PwCOregon Institute of Technology

Co-founded a Digital Loss Prevention (DLP) startup and raised $6M in seed funding by showcasing a controlled, laptop-based technology demo. Post-funding, drove MVP planning and execution by sequencing operations and assembling a team to build an appliance MVP, using an iterative build/evaluate/visualize approach.

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SS

Sahithi S

Screened

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

Texas, USA6y exp
NVIDIAKennesaw State University

Built and deployed a production generative AI chatbot at NVIDIA using LangChain + GPT-3 integrated with internal data sources, cutting response time nearly in half and improving CSAT by ~12 points. Also delivered LLM-driven QA tools by fine-tuning Hugging Face transformer models and deploying via an AWS-based pipeline (Lambda/Glue/S3) with orchestration (Airflow/Step Functions), CI/CD, Kubernetes, and monitoring (MLflow/Splunk/Power BI).

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SK

Sahithi K

Screened

Mid-level Data Engineer specializing in cloud data platforms and streaming pipelines

Boston, MA4y exp
ModernaUniversity of Massachusetts Dartmouth

Data engineer with experience at Moderna and Block owning high-volume (≈10TB/day) production pipelines on AWS, using Kafka/S3/Glue/dbt/Snowflake with strong data quality and observability practices (schema validation, anomaly detection, CloudWatch monitoring). Also built external financial API ingestion with Airflow retries, throttling/token rotation, and schema versioning, and helped stand up an early-stage biomedical data platform with CI/CD and incident debugging.

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Byron Pineda - Staff/Lead Data Scientist specializing in Generative AI, NLP/LLMs, and MLOps in Pascagoula, MS

Byron Pineda

Screened

Staff/Lead Data Scientist specializing in Generative AI, NLP/LLMs, and MLOps

Pascagoula, MS10y exp
TuringMississippi State University

Lead Data Scientist (10+ years) with recent work in healthcare data: built production pipelines that unify EHR, genomics, and clinical notes using NLP (spaCy/BERT/BioBERT) and scalable Spark-based processing. Also led development of domain-specific LLM/NLP systems for chatbots and semantic search, deploying models via FastAPI/Flask and improving retrieval with FAISS-backed, fine-tuned clinical embeddings and RAG-style workflows.

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