Senior MLOps Engineer
Intelmatix
Posted on 5 Apr
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Nationality
Any Nationality
Gender
Not Mentioned
Vacancy
1 Vacancy
Job Description
Roles & Responsibilities
Own the full ML lifecycle develop, deploy, monitor, and continuously improve models and infrastructure.
Build and optimize CI/CD pipelines for both software and ML workflows, ensuring smooth end-to-end automation.
Design, deploy, and manage cloud infrastructure (preferrably AWS) to support training, serving, and scaling ML workloads.
Automate deployment and orchestration, managing environments with modern DevOps/MLOps tools and best practices.
Develop, orchestrate, and deploy LLM workflows, ensuring they are production-ready, scalable, and reliable.
Ensure reliability and scalability of production systems while maintaining monitoring and observability, performance, and security.
Collaborate across teams, working closely with software engineers, data scientists, and product managers to deliver ML-powered features.
Proactively troubleshoot and resolve issues, from infrastructure bottlenecks to model performance concerns.
Document and standardize workflows, infrastructure, and operational procedures for reproducibility and clarity.
Drive innovation and adopt best practices in MLOps, DevOps, and infrastructure engineering to continuously enhance systems.
Desired Candidate Profile
Experience: 5+ years in MLOps, DevOps, or infrastructure-focused engineering roles, delivering and operating ML projects in production.
Programming & ML: Strong proficiency in Python, deep understanding of cutting edge ML concepts and understanding of ML/LLM workflows and constraints.
Cloud & Infrastructure: Hands-on experience with AWS, including ECS, and designing scalable, secure, and observable cloud architectures for ML workloads.
Containerization & Orchestration: Expertise with Docker, Kubernetes, and managing multi-stage ML workflows.
CI/CD & DevOps Tools: Experience with GitHub Actions, infrastructure as code, automated testing, and monitoring tools like Prometheus, Grafana, Loki, and Opentelemetry.
ML Platform Tools: Familiarity with ML platforms, workflow orchestration, experiment tracking, and building production-grade APIs for serving models (e.g., MLflow, Prefect, FastAPI, or equivalents).
LLM Development & Deployment: Hands-on experience with LLM workflows, including model orchestration, deployment, and serving using tools like LangGraph, Flowise, or equivalents.
Data & Monitoring: Knowledge of ETL pipelines, large-scale data processing, observability, telemetry, and alerting best practices.
Collaboration & Communication: Ability to work with research engineers, software teams, and product managers, translating technical constraints into production-ready solutions.
Mindset: Balances reliability with velocity, ensuring reproducible, secure, and high-performing ML systems while enabling teams to ship fast.
Company Industry
- IT - Software Services
Department / Functional Area
- IT Software
Keywords
- Senior MLOps Engineer
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