ML OPS (DevOps)

• Prohibited locations: RF, Ukraine, RB

• English: B2

• Years: ML - 6+ months, DevOps -3+ years commercial experience

Key Responsibilities

• Development and support of end-to-end ML pipelines (training, validation, deployment, monitoring, retraining)

• Construction and operation of CI/CD for models (test automation, packaging, and deployment)

• Design of LLM/RAG pipelines, context management,

• embedding dashboards (embedding quality/dynamics dashboards), index regeneration, prompt and fact-check testing (Grounding/citation)

• MLOps platform setup: experiment tracking, model registry, feature store, monitoring

• Management of ML infrastructure and environments (GPU/CPU pools, Kubernetes/EKS, Docker)

• Implementation of deployment strategies: canary, shadow, A/B testing

• Ensuring model quality monitoring (accuracy drift, data drift, PSI, SLO/SLA)

• Artifact management (data, models, metadata, versions)

• Security compliance (encryption, access control, auditing, operation in private VPCs)

• Integrating ML models into backend services (API, gRPC, REST)

• Collaborating with Data Engineering and Data Science teams

• Documenting processes and best practices for ML infrastructure

• Managing the cost and scaling of ML infrastructure in AWS

• Data governance: storage policies (S3 lifecycle), dataset versioning (DVC/LakeFS), data lineage (OpenLineage), quality gates in CI/CD

Requirements

ML Ops Tools

• MLflow or Kubeflow (experiments, registry)

• Feature Store (Feast, Tecton, or custom)

• Airflow, Prefect, or Kubeflow Pipelines (ML workflow orchestration)

Infrastructure and Containerization

• Docker, Kubernetes/EKS

• AWS S3, ECR, EKS, IAM, KMS, VPC

• Terraform or Pulumi (IaC)

• GitHub Actions, GitLab CI, or Jenkins (CI/CD)

• Autoscaling, AWS Batch/Step Functions for offline processing and retrieval

Monitoring and Observability

• Prometheus, Grafana, CloudWatch, CloudTrail

• Model Quality Metrics (AUC, F1, Brier, logloss)

• Stability metrics (drift detection, PSI)

• LLM-specific metrics: tokens/sec, context length, prompt/response size, grounding rate, citation coverage, hallucination rate.

Key Competencies

• Building a stable and secure ML infrastructure

• Automation Full-cycle ML: from data to inference services

• Quality control and stability of models in production

• Effective collaboration with data science and data engineering teams

Joining Valletta Software Development means:

• 🌍 A Global, Thriving Team

• Join 100+ specialists from 20+ countries, united by a passion for outstanding

• IT solutions.

• 🚀Diverse projects: Fintech, MedTech, AI/ML, e-commerce, and more. Switch

• teams or industries to broaden your skills.

• 💡 Support at Every Step Client interview prep: We train you to succeed + give actionable feedback.

• ✔️ Strategic stability: Well-structured processes, strong management, and long- term vision.

• ✔️ Core values: Honesty, flexibility, innovation, and a people-first approach.

• 💸 Regular salary review based on your personal results

• ✨ Paid rest days and sick leaves;