Senior Mlops/llmops Engineer

Senior MLOps/LLMOps Engineer

We're an AI-first global tech company with 25+ years of engineering leadership, 2,000+ team members, and 500+ active projects powering Fortune 500 clients, including HBO, Microsoft, Google, and Starbucks.

From AI platforms to digital transformation, we partner with enterprise leaders to build what's next.

What powers it all? Our people are ambitious, collaborative, and constantly evolving.

About the Client

The customer is one of the largest online gambling companies in the world, with over 26 million clients across all markets. The company was founded in 1997 and listed on Nasdaq Stockholm in 2004. They are committed to offering their clients the best possible deal and user experience, while ensuring a safe and fair gambling environment.

What You'll Do

Platform & Deployment

• Manage and evolve ML/LLM infrastructure on Kubernetes/EKS (CPU/GPU) for multi-tenant workloads across AWS/Azure, ensuring region-aware scheduling, cross-region access, and artifact management
• Provision cloud environments, maintain deployment workflows, and build GitOps-native pipelines (GitLab CI, Jenkins, ArgoCD, Helm, FluxCD) for fast, safe rollouts

LLM Operations & Optimization

• Deploy, scale, and optimize LLMs (GPT, Claude, etc.) with attention to prompt engineering, performance, and cost
• Operate Argo Workflows for data prep, model training, and batch compute, and track model performance and drift via AI observability frameworks

CI/CD & Infrastructure as Code

• Design robust CI/CD pipelines across dev, staging, and production. Implement IaC with Terraform, CloudFormation, and Helm
• Manage container orchestration, secrets, and secure deployments

Observability & Reliability

• Set up monitoring with Prometheus/Grafana, Splunk, CloudWatch, and ELK
• Implement alerting strategies, troubleshoot production issues, and ensure high availability

Data Platform & Reproducibility

• Build and maintain data pipelines and platforms (Apache Iceberg) for reproducible ML experiments, lineage tracking, and automated governance
• Collaborate with data engineers for seamless integration with model training workflows

Developer Experience & Enablement

• Create APIs, CLIs, and UIs for self-serve infrastructure. Provide documentation, templates, and best practices
• Treat the ML platform as a product, gathering feedback and improving usability

Architecture, Security & Governance

• Define scalable, secure, and compliant platform architecture. Implement FinOps practices, cost monitoring, and multi-tenant optimization
• Drive CI/CD culture and continuous improvement across teams

What You Bring

• 8+ years in DevOps, Platform Engineering, or SRE, including 2+ years in MLOps/LLMOps
• Hands-on experience with AWS (Bedrock, S3, EC2, EKS, RDS/PostgreSQL, ECR, IAM, Lambda, Step Functions, CloudWatch) and Kubernetes workloads, including GPU, autoscaling, and multi-tenant configurations
• Skilled in container orchestration, secrets management, and GitOps deployments (Jenkins, ArgoCD, FluxCD)
• Experience deploying and scaling LLMs (GPT, Claude-family), with prompt engineering and performance optimization
• Strong Python skills (FastAPI, Django, Pydantic, boto3, Pandas, NumPy) and solid ML framework knowledge (scikit-learn, TensorFlow, PyTorch)
• Proficient in building reproducible data pipelines, IaC (Terraform, CloudFormation, Helm), CI/CD pipelines, and observability (Prometheus/Grafana, Splunk, Datadog, OpenTelemetry)
• Strong networking, security, and Linux fundamentals. Excellent communicator, self-motivated, and focused on improving developer experience

Nice to Have

• Experience with distributed compute frameworks such as Dask, Spark, or Ray
• Familiarity with NVIDIA Triton, TorchServe, or other inference servers
• Experience with ML experiment tracking platforms like Weights & Biases, MLflow, or Kubeflow
• FinOps best practices and cost attribution strategies for multi-tenant ML infrastructure
• Exposure to multi-region and multi-cloud designs, including dataset replication strategies, compute placement, and latency optimization
• Experience with LakeFS, Apache Iceberg, or Delta Lake for data versioning and lakehouse architectures
• Knowledge of data transformation tools such as DBT
• Experience with data pipeline orchestration tools like Airflow or Prefect
• Familiarity with Snowflake or other cloud data warehouses
• Understanding of responsible AI practices, model governance, and compliance frameworks

English Level

Intermediate+

Legal & Hiring Information

• Exadel is proud to be an Equal Opportunity Employer committed to inclusion across minority, gender identity, sexual orientation, disability, age, and more
• Reasonable accommodations are available to enable individuals with disabilities to perform essential functions
• Please note: this job description is not exhaustive. Duties and responsibilities may evolve based on business needs

Your Benefits at Exadel

Exadel benefits vary by location and contract type. Your recruiter will fill you in on the details.

• International projects
• In-office, hybrid, or remote flexibility
• Medical healthcare
• Recognition program
• Ongoing learning & reimbursement
• Well-being program
• Team events & local benefits
• Sports compensation
• Referral bonuses
• Top-tier equipment provision

Exadel Culture

We lead with trust, respect, and purpose. We believe in open dialogue, creative freedom, and mentorship that helps you grow, lead, and make a real difference. Ours is a culture where ideas are challenged, voices are heard, and your impact matters.