MLOps Engineer

As an MLOps Engineer, you will help our clients with automating and managing the...

Location

Belgium , Brussels

Salary:

Not provided

Sopra Steria

Expiration Date

Until further notice

Requirements

At least 3 years of experience as an MLOps Engineer, Machine Learning Engineer or in a similar position
Experience managing the machine learning lifecycle in production, and related MLOps frameworks
Master CI/CD pipelines and Infrastructure as Code
In depth knowledge of containers and container orchestration
Solid understanding of Linux and in depth knowledge of at least one cloud provider
Programming experience with Python
Experience with monitoring and logging technologies
Good grasp of big data technologies such as Apache Spark, ElasticSearch, Kafka
Able to coach others and give technical advice and direction
Understanding of networking, firewalls, and IT infrastructure

Job Responsibility

Design and implement solutions to automate the experimentation and release cycle of machine learning, from traditional ML to LLMs
Identify, design, and implement process improvements in the machine learning lifecycle: automating manual processes, optimizing data delivery, re-design infrastructure for greater scalability, etc
Collaborate with Data Scientists, Machine Learning Engineers, Data Engineers, and other IT roles to integrate all parts of the solution
Implement CI/CD pipelines, IaC, monitoring for models and infrastructure

What we offer

A variety of perks, such as mobility options (including a company car), insurance coverage, meal vouchers, eco-cheques, and more
Continuous learning opportunities through the Sopra Steria Academy to support your career development
The opportunity to connect with fellow Sopra Steria colleagues at various team events

Fulltime

Staff MLOps Engineer

At Inworld, we’re building the AI framework behind the next generation of real-t...

Location

Canada , Vancouver

Salary:

190000.00 - 240000.00 CAD / Year

Inworld AI

Expiration Date

Until further notice

Requirements

7+ years of software engineering experience
5+ years of infrastructure-as-code
Proficiency in managing Kubernetes clusters and applications, including creating Helm charts/Kustomize manifests for new applications
Experience in creating and maintaining CI/CD pipelines for both applications and infrastructure deployments (using tools like Terraform/Terragrunt, ArgoCD, GitHub Actions, Ansible, etc.)
Deep knowledge of at least one major cloud provider (Google Cloud Platform, Microsoft Azure, Oracle Cloud)
Proficient in at least one backend programming/scripting languages such as Golang, Python, and Bash
Knowledge of SLURM or similar job schedulers for distributed training
Experience with data pipeline and workflow management tools
Desire to work at a fast-growing Series A startup, comfortable with uncertainty, owning and scaling new products, and embracing an experimental and iterative development process

Job Responsibility

Build and scale MLOps systems to streamline the end-to-end ML model lifecycle on the Inworld AI platform, from training to deployment
Design and implement robust model training, evaluation, and release pipelines
Collaborate cross-functionally with ML and backend teams to design, deploy, and maintain scalable secure infrastructure for Inworld’s AI Engine and Studio
Facilitate a "you build it, you run it" culture by providing the necessary tools and processes for monitoring the reliability, availability, and performance of services
Manage CI/CD pipelines to ensure smooth and efficient code integration and deployment
Identify and implement opportunities to enhance engineering speed and efficiency
Provide technical leadership in ML engineering best practices, raise the technical bar, and mentor junior engineers in MLOps principles

What we offer

equity
benefits

Fulltime

Staff MLOps Engineer

At Inworld, we’re building the AI framework behind the next generation of real-t...

Location

United States , Mountain View

Salary:

180000.00 - 280000.00 USD / Year

Inworld AI

Expiration Date

Until further notice

Requirements

7+ years of software engineering experience, with 5+ years of infrastructure-as-code
Proficiency in managing Kubernetes clusters and applications, including creating Helm charts/Kustomize manifests for new applications
Experience in creating and maintaining CI/CD pipelines for both applications and infrastructure deployments (using tools like Terraform/Terragrunt, ArgoCD, GitHub Actions, Ansible, etc.)
Deep knowledge of at least one major cloud provider (Google Cloud Platform, Microsoft Azure, Oracle Cloud)
Proficient in at least one backend programming/scripting languages such as Golang, Python, and Bash
Knowledge of SLURM or similar job schedulers for distributed training
Experience with data pipeline and workflow management tools
Desire to work at a fast-growing Series A startup, comfortable with uncertainty, owning and scaling new products, and embracing an experimental and iterative development process
In-office location: Mountain View, CA, United States. You must be available for hybrid work

Job Responsibility

Build and scale MLOps systems to streamline the end-to-end ML model lifecycle on the Inworld AI platform, from training to deployment
Design and implement robust model training, evaluation, and release pipelines
Collaborate cross-functionally with ML and backend teams to design, deploy, and maintain scalable secure infrastructure for Inworld’s AI Engine and Studio
Facilitate a "you build it, you run it" culture by providing the necessary tools and processes for monitoring the reliability, availability, and performance of services
Manage CI/CD pipelines to ensure smooth and efficient code integration and deployment
Identify and implement opportunities to enhance engineering speed and efficiency
Provide technical leadership in ML engineering best practices, raise the technical bar, and mentor junior engineers in MLOps principles

What we offer

equity and benefits

Fulltime

Senior MLOps Engineer

A fast-growing, VC-backed AI company is seeking a Senior MLOps Engineer to help ...

Location

United States

Salary:

Not provided

Orbis Consultants

Expiration Date

Until further notice

Requirements

4+ years of experience in MLOps with hands-on ownership of production ML systems
Degree in Computer Science or a related field (Bachelor’s or Master’s)
Strong expertise in Python and ML libraries (TensorFlow, PyTorch, or similar)
Experience with ML lifecycle management tools
Practical experience deploying and monitoring deep learning models
Proficiency with cloud platforms (AWS, Azure, or GCP)

Job Responsibility

Design, build, and maintain infrastructure for deploying, monitoring, and updating ML models in production
Automate end-to-end pipelines covering ingestion, preprocessing, training, validation, and deployment
Implement monitoring for model performance, drift, latency, and accuracy
Ensure ML systems are secure, scalable, and cost-efficient
Partner with ML engineers, data scientists, and product teams to move models seamlessly from research to production
Apply software engineering best practices—including CI/CD, testing, and version control—to ML workflows
Continuously improve documentation, tooling, and infrastructure

What we offer

great equity and the chance to join a rocketship

Fulltime

Data Engineer Senior

We are looking for a highly skilled professional to lead the industrialisation o...

Location

Portugal , Lisbon

Salary:

Not provided

Inetum

Expiration Date

Until further notice

Requirements

Minimum of 5 years’ experience in MLOps, data engineering, or DevOps with a focus on ML/DL/LLM/AI agents in production environments
Strong proficiency in Python
Hands-on experience with CI/CD tools such as GitLab, Docker, Kubernetes, Jenkins
Solid understanding of ML, DL, and LLM models
Experience with ML lifecycle tools such as MLflow or DVC
Good understanding of model lifecycle, data traceability, and governance frameworks
Experience with on-premise and hybrid infrastructures
Excellent communication skills and ability to collaborate with remote teams
Proactive mindset, technical rigour, and engineering mentality
Willingness to learn, document, and standardise best practices

Job Responsibility

Analyse, monitor, and optimise ML models, tracking their performance
Design and implement CI/CD pipelines for ML models and data flows
Containerise and deploy models via APIs, batch processes, and streaming
Manage model versioning and traceability
Ensure continuous improvement and adaptation of AI use cases and ML models
Set up monitoring and alerting for model performance
Establish incident response protocols in collaboration with IT
Maintain dashboards and automated reports on model health
Implement validation frameworks for data and models (e.g., Great Expectations, unit tests, stress tests), in collaboration with Group Governance
Contribute to documentation and apply technical best practices

What we offer

Work in a constantly evolving environment
Contribute to digital impact
Opportunity for growth and development

Fulltime

Machine Learning Platform / Backend Engineer

We are seeking a Machine Learning Platform/Backend Engineer to design, build, an...

Location

Serbia; Romania , Belgrade; Timișoara

Salary:

Not provided

Everseen

Expiration Date

Until further notice

Requirements

4-5+ years of work experience in either ML infrastructure, MLOps, or Platform Engineering
Bachelors degree or equivalent focusing on the computer science field is preferred
Excellent communication and collaboration skills
Expert knowledge of Python
Experience with CI/CD tools (e.g., GitLab, Jenkins)
Hands-on experience with Kubernetes, Docker, and cloud services
Understanding of ML training pipelines, data lifecycle, and model serving concepts
Familiarity with workflow orchestration tools (e.g., Airflow, Kubeflow, Ray, Vertex AI, Azure ML)
A demonstrated understanding of the ML lifecycle, model versioning, and monitoring
Experience with ML frameworks (e.g., TensorFlow, PyTorch)

Job Responsibility

Design, build, and maintain scalable infrastructure that empowers data scientists and machine learning engineers
Own the design and implementation of the internal ML platform, enabling end-to-end workflow orchestration, resource management, and automation using cloud-native technologies (GCP/Azure)
Design and manage Kubernetes-based infrastructure for multi-tenant GPU and CPU workloads with strong isolation, quota control, and monitoring
Integrate and extend orchestration tools (Airflow, Kubeflow, Ray, Vertex AI, Azure ML or custom schedulers) to automate data processing, training, and deployment pipelines
Develop shared services for model behavior/performance tracking, data/datasets versioning, and artifact management (MLflow, DVC, or custom registries)
Build out documentation in relation to architecture, policies and operations runbooks
Share skills, knowledge, and expertise with members of the data engineering team
Foster a culture of collaboration and continuous learning by organizing training sessions, workshops, and knowledge-sharing sessions
Collaborate and drive progress with cross-functional teams to design and develop new features and functionalities
Ensure that the developed solutions meet project objectives and enhance user experience

Fulltime

Senior/Architect Data Engineer

We are seeking a highly skilled and experienced Senior/Architect Data Engineer t...

Location

Poland , Warsaw; Poznań; Lublin; Katowice; Rzeszów

Salary:

Not provided

Inetum

Expiration Date

Until further notice

Requirements

Proven experience architecting solutions on the Databricks Lakehouse using Unity Catalog, Delta Lake, MLflow, Model Serving, Feature Store, AutoML, and Databricks Workflows
Expertise in real-time/low latency model serving architectures with auto-scaling, confidence-based routing, and A/B testing
Strong knowledge of cloud security and governance on Azure or AWS, including Azure AD/AWS IAM, encryption, audit trails, and compliance frameworks
Hands-on MLOps skills across experiment tracking, model registry/versioning, drift monitoring, automated retraining, and production rollout strategies
Proficiency in Python and Databricks native tooling, with practical integration of REST APIs/SDKs and Databricks SQL in analytics products
Familiarity with React dashboards and human-in-the-loop operational workflows for ML and data quality validation
Demonstrated ability to optimize performance, reliability, and cost for large-scale analytics/ML platforms with strong observability
Experience leading multi-phase implementations with clear success metrics, risk management, documentation, and training/change management
Domain knowledge in telemetry, time series, or industrial data (aerospace a plus) and prior work with agentic patterns on Mosaic AI
Databricks certifications and experience in enterprise deployments of the platform are preferred

Job Responsibility

Lead the design and implementation of a Databricks-centric multi-agent processing engine
Design governed data ingestion, storage, and real-time processing workflows using Delta Lake, Structured Streaming, and Databricks Workflows
Own the model lifecycle with MLflow, including experiment tracking, registry/versioning, A/B testing, drift monitoring, and automated retraining pipelines
Architect low latency model serving endpoints with auto-scaling and confidence-based routing for sub-second agent decisioning
Establish robust data governance practices with Unity Catalog, including access control, audit trails, data quality, and compliance
Drive performance and cost optimization strategies, including auto-scaling, spot usage, and observability dashboards
Define production release strategies (blue-green), monitoring and alerting mechanisms, operational runbooks, and Service Level Objectives (SLOs)
Partner with engineering, MLOps, and product teams to deliver human-in-the-loop workflows and dashboards
Lead change management, training, and knowledge transfer while managing a parallel shadow processing path
Plan and coordinate phased delivery, success metrics, and risk mitigation

What we offer

Flexible working hours
Hybrid work model
Cafeteria system
Generous referral bonuses (up to PLN6,000)
Additional revenue sharing opportunities
Ongoing guidance from dedicated Team Manager
Tailored technical mentoring from assigned technical leader
Dedicated team-building budget for online and on-site team events
Opportunities to participate in charitable initiatives and local sports programs
Supportive and inclusive work culture

Fulltime

AI Engineer

As an AI Engineer, you bring traditional and Generative AI into real world use c...

Location

Belgium , Brussels/Flanders

Salary:

Not provided

Sopra Steria

Expiration Date

Until further notice

Requirements

At least 3 years of experience as a Machine Learning Engineer, Data Scientist, MLOps Engineer, or in a similar position
Experience with traditional NLP, LLMs, and conversational AI, both in experimentation phase and in production
Experience with vector databases for semantic search and RAG solutions in a production environment
Strong software engineering background and ability to write production ready code in Python
Experience with experiment tracking, models monitoring, LLM and NLP evaluation techniques, and deployment strategies
Comfortable with the machine learning lifecycle and MLOps and DevOps principles
Experience with at least one cloud provider and good knowledge of the data ecosystem
Able to coach others, give technical advice and direction, and work independently
Master or PhD in Machine Learning, Artificial Intelligence, Computer Engineering, or related field
Proficient in English, knowledge of Dutch and/or French is a plus.

Job Responsibility

Design and implement solutions that require from traditional AI to LLMs, from semantic search to conversational AI
Train, fine-tune, improve, and deploy ML models
Write production ready code to serve online, batch, and real time models
Build applications and software to serve AI driven use cases
Work in close collaboration with Data Scientists, MLOps Engineers and Data Engineers to integrate all parts of the solution
Help build solutions and/or operate with clients on a medium to long-term basis.

What we offer

Mobility options (including a company car)
insurance coverage
meal vouchers
eco-cheques
continuous learning opportunities through the Sopra Steria Academy
opportunity to connect with fellow Sopra Steria colleagues at various team events.

MLOps Engineer - Implementation

BMW

Location:
Germany , Munich

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Job Description:

Job Responsibility:

Requirements:

Nice to have:

Additional Information:

Job Posted:
March 21, 2026

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Job Posted:
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