MLOps Engineer
Andersen is hiring an MLOps Engineer for a project delivering scalable digital solutions and supporting reliable AI and technology platforms across diverse organizations.
The customer is a government-backed organization focused on socio-economic development through digital and business solutions. They aim to improve labor markets, enhance employment opportunities, and support public and private sector growth. Their services include digital platforms, automation, and training programs to develop human capital.
The project is focused on delivering digital and technology solutions for government, semi-government, and private-sector organizations. It supports diverse business needs through the development and implementation of reliable, scalable, and efficient solutions.
Responsibilities:
Designing and owning the end-to-end MLOps architecture for production Machine Learning and Generative AI systems.
Building and maintaining ML training, validation, deployment, serving, monitoring, and retraining pipelines.
Defining and implementing the model promotion lifecycle across development, staging, and production environments.
Building and maintaining model registry, experiment tracking, dataset/model versioning, and reproducible ML workflows.
Designing scalable training and inference infrastructure, including GPU-backed workloads.
Building and maintaining CI/CD pipelines for ML and AI workloads with automated testing, quality gates, approval controls, and rollback mechanisms.
Implementing progressive delivery approaches for ML models, including shadow testing, canary releases, and blue-green deployments.
Collaborating closely with Platform Engineering to run AI workloads on shared Kubernetes infrastructure.
Translating ML infrastructure requirements into technical requirements for platform, security, capacity, and architecture teams.
Establishing reusable MLOps project templates, shared pipeline components, and engineering standards.
Implementing automated ML testing, including data validation, model regression tests, training-serving consistency checks, and evaluation gates.
Owning production reliability of ML systems, including availability, latency, throughput, scalability, and operational stability.
Building observability across infrastructure, data quality, model performance, and business impact.
Configuring monitoring, alerting, incident response, runbooks, rollback procedures, and post-incident reviews for AI systems.
Implementing automated model retraining based on schedules, events, and data/model drift.
Managing the full model lifecycle, including deployment, monitoring, retraining, version promotion, and retirement.
Tracking and optimizing infrastructure, training, inference, and GPU-related costs.
Building and operating the production layer for Generative AI and LLM-based applications.
Implementing LLM model gateways, routing, prompt management, prompt versioning, caching, and rate limiting.
Implementing LLM guardrails, groundedness monitoring, sensitive-data protection, and prompt-injection mitigation.
Building observability and permission controls for agentic AI systems.
Implementing token-level and request-level consumption metering across Generative AI applications.
Building cost attribution mechanisms by business unit, use case, application, model, tenant, and environment.
Optimizing LLM workloads through model routing, prompt/context optimization, caching, batching, and selection of cost-efficient models.
Applying hands-on Generative AI engineering practices, including prompt engineering, structured outputs, context management, embeddings, tool/function calling, and model adaptation where required.
Evaluating emerging ML and Generative AI tools, models, and platforms and recommend technologies for adoption.
Making and documenting architectural and build-vs-configure decisions for new AI capabilities.
Producing architecture decision records, reference designs, technical documentation, and operational guidelines.
Participating in architecture, capacity planning, technical roadmap, and platform strategy discussions.
Performing code reviews, technical mentoring, pairing, and knowledge sharing to improve engineering standards across the team.
Must-haves:
Strong engineering experience across Software Engineering, DevOps/SRE, and MLOps for 5+ years.
Production ownership of machine learning systems: you have operated, supported, and been accountable for ML in production.
Deep hands-on experience with Docker and Kubernetes, including resource management and GPU workloads.
Strong proficiency with GitLab CI, GitHub Actions, ArgoCD, or equivalent, including pipeline-enforced quality gates.
Extensive experience with MLflow, Kubeflow, Feast, BentoML, KServe, SageMaker, Vertex AI, Databricks, or comparable platforms.
Python at production engineering standard (typed, tested, packaged, and reviewed).
Infrastructure as Code (Terraform, Ansible, or equivalent, with real module and state management experience).
Production service engineering (stateless, configuration-driven services with structured logging, published API contracts, and centrally managed secrets).
Data services in production (PostgreSQL, caching, and message-driven asynchronous Processing).
Monitoring and observability (metrics, logging, error tracking, and ML-aware monitoring such as Evidently, WhyLabs, or Arize).
Proven collaboration with platform or infrastructure engineering teams, building on shared infrastructure rather than around it.
Hands-on LLMOps experience: model gateways, prompt versioning, RAG pipelines, vector databases, evaluation harnesses, and guardrail frameworks.
Generative AI consumption management: token metering, cost attribution across multiple applications or tenants, budget and quota controls, and demonstrable cost optimization of LLM workloads.
Model evaluation and selection: building evaluation suites that objectively compare models on quality, latency, cost, and safety for a given task, and using them to drive adoption decisions.
Practical generative AI engineering: prompt and structured output design, context management, embedding selection, tool calling, and fine-tuning or model adaptation.
Experience operating agentic AI systems in production.
Experience establishing an ML platform capability from an early stage.
Cost engineering for AI workloads: GPU efficiency, inference optimization, and consumption attribution.
Familiarity with AI governance and regulated environments - model risk management, auditability, and fairness testing.
Experience with data quality frameworks and workflow orchestration.
Level of English - Upper-Intermediate and above.
Reasons why this job would be interesting to you:
Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc..
The opportunity to change the project and/or develop expertise in an interesting business domain.
Job conditions - you can work both fully remotely and from the office or can choose a hybrid variant.
Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee.
The opportunity to earn up to an additional 1,000 EUR per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities.
Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated.
Bright corporate life (parties / pizza days / PlayStation / fruits / coffee / snacks / movies).
Certification compensation (AWS, PMP, etc).
Referral program.
Private health insurance and sports compensation, depending on the type of employment.
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