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Senior Software Engineer ML - Contractor position
Senior ML engineer to take ML/LLM proofs-of-concept to production, build MLOps pipelines, and deploy RAG systems for Fortune 500 clients.
Machine Learning Engineer
Build, deploy, and scale AI/ML models and pipelines for Wave’s fintech platform, ensuring reliability, governance, and integration with AWS and MLOps tools.
Solutions Architect
Design and implement cloud infrastructure and MLOps solutions for AI teams, advising clients on GPU cloud technologies and optimizing ML pipelines using tools like Terraform, Kubernetes, and Python.
Manager ai products
Role : The Manager AI Products is the primary builder within the AI Hub, responsible for turning architectural designs and product specifications into working, production-deployed AI systems. This role spans the full…
GCP Data & MLOps Engineer — Pipelines & AI
Designs and builds scalable GCP-based data pipelines, MLOps workflows, and cloud-native apps using Dataflow, Kubeflow, BigQuery, Python, and Django.
GCP Data Engineer
Build and maintain GCP data pipelines and MLOps frameworks using Dataflow, Apache Beam, BigQuery, Kubeflow, and Python for enterprise clients.
DevOps engineer (Внедрение ML платформы)
DevOps engineer to build and maintain CI/CD, monitoring, and Kubernetes-based infrastructure for an internal ML platform used for training models and AI agents.
Full-stack Software Engineer (IT-CA-IR-2026-199-GRAE)
Build and maintain Jupyter extensions for CERN’s research platforms (Python, JupyterLab/React) and contribute to a Kubernetes-based Virtual Research Environment used by thousands of scientists.
Staff Machine Learning Engineer, Generative AI, Voice & Speech
Lead the design and deployment of large-scale generative AI voice and speech systems, building reusable ML infrastructure and guiding cross-functional teams to integrate cutting-edge AI into products.
Full-Stack Software Engineer (IT-CA-IR-2026-198-GRAE)
Build and harden Jupyter extensions for CERN’s research platforms (Rucio, REANA, Zenodo) and deploy them on Kubernetes, integrating with SWAN and EOSC to create a unified, open analysis environment for scientists.
AI Architect
Designs and implements enterprise-grade GenAI and Agentic AI solutions using LLMs, vector databases, and cloud platforms for client engagements.
ИТ-Лидер кластера MLOps SberWorks
Leads a team building and operating an MLOps platform and multi-agent environment, overseeing the full ML lifecycle, AI agent integration, and scalable infrastructure for a large corporation.
Data Platform Engineer
Build and maintain ML feature pipelines for batch, streaming, and nearline scenarios to power recommendations and analytics at a top Russian streaming platform.
AI Enterprise Architect
Design and lead end-to-end AI infrastructure, from GPU clusters and high-performance networking to MLOps and governance frameworks, ensuring scalable, compliant, and production-ready AI systems.
Senior AI Engineer
Build and scale dunnhumby’s Enterprise AI Platform, designing production-grade AI systems including RAG, agentic workflows, and LLM-powered services using Python, LangChain, and cloud-native tools.
DevOps engineer (Внедрение ML платформы)
DevOps engineer builds and maintains CI/CD, monitoring, and Kubernetes-based infrastructure for a company-wide ML platform used in decision-making, risk metrics, forecasting, and AI agents.
Data Science - Senior Architect (Healthcare & Life Sciences)
Architect and lead enterprise AI solutions for healthcare and life sciences, including GenAI/RAG systems, multi-agent workflows, and MLOps pipelines.
Senior ai/ml engineer — build production ai for igaming
Senior AI/ML Engineer builds and deploys production AI systems for an iGaming company, focusing on recommendations, LLM apps, and agents using AWS SageMaker, Airflow, Kubeflow, and LangChain.
MLOps Engineer
Build and maintain ML pipelines and AI infrastructure for banking products, using Kubernetes, Docker, Terraform, and cloud platforms to deploy and monitor ML models at scale.
Software Development Engineer in Test
Build and maintain automated tests and CI/CD pipelines for distributed AI infrastructure running on GPU/CPU clusters, Kubernetes, and Slurm.