--- title: "Senior MLOps Engineer" company: "N-iX" company_url: "https://www.remjobs.works/companies/n-ix" url: "https://www.remjobs.works/job/n-ix-senior-mlops-engineer-4ef0efe2-bf2e-422c-b671-625f85885754" apply_url: "https://careers.n-ix.com/jobs/4982967101?gh_jid=4982967101" workplace: onsite location: "Ukraine" employment_type: unspecified seniority: senior role: software-engineering region: europe skills: ["aws", "docker", "kubernetes", "llm", "python", "spark", "terraform"] date_posted: 2026-09-24T13:06:35.000Z first_seen_by_remjobs: 2026-09-24T13:11:10.724Z --- # Senior MLOps Engineer **N-iX** · Ukraine Apply: https://careers.n-ix.com/jobs/4982967101?gh_jid=4982967101 ## About the role **Client Overview:** Our client is an Azerbaijani telecommunications company, the largest mobile network operator in Azerbaijan. The main products are: Fixed telephony, Mobile telephony, Internet services, Wireless broadband, and Value-added services. **Project Objectives:** The primary goal is to accelerate the client’s Data & AI initiatives via a secure, hybrid cloud foundation on AWS while systematically modernizing the IT estate as part of the cloud migration. Key Project Objectives include: - Cloud Foundation & Landing Zone: Deploy target hybrid network architectures, establishing a secure Landing Zone and hybrid Data/AI platforms on AWS. - Security, Compliance & Governance: Operationalize on-prem tokenization (achieving zero raw PII in the cloud), resolve policy blockers to include AWS in the ISMS, and establish a Cloud Center of Excellence (CCoE) to govern Cloud adoption. - AI Chatbot & Voicebot Design & Implementation: Develop and operationalize a flagship Customer Care Chatbot and Voicebot as the first hybrid-setup consumer. ###### Responsibilities: - Build, operationalize, and automate end-to-end MLOps pipelines using Amazon SageMaker Pipelines and MLflow for experiment tracking, model versioning, and registry lifecycle management. - Design, deploy, and manage production SageMaker inference endpoints (real-time, serverless, and batch) and Amazon Bedrock API integrations for LLM/SLM deployment with cost controls and latency optimization (Bedrock API Gatekeeper). - Implement AgentOps / LLMOps frameworks (AgentCore, Bedrock Guardrails, Promptfoo) to manage multi-agent orchestration, prompt evaluation, safety guardrails, and RAG retrieval pipelines. - Operationalize real-time STT / TTS (Speech-to-Text / Text-to-Speech) voicebot pipelines and low-latency speech inference on hybrid/cloud GPU node pools for the flagship Customer Care Voicebot. - Optimize specialized GPU node pools (NVIDIA A100/L40S / EC2 GPU instance types) for Azerbaijani SLM/LLM model training, fine-tuning, and scalable inference workloads. - Establish automated CI/CD for Machine Learning using GitLab CI/CD pipelines and Infrastructure-as-Code (Terraform or AWS CDK) to enforce security-gated MLOps promotion workflows (from SageMaker Canvas/Sandbox to production). - Integrate data de-identification, Format Preserving Encryption (FPE), and tokenization wrappers into ML data pipelines to ensure zero raw PII enters AWS cloud environments during model training and inference. - Set up telemetry, performance monitoring, model drift detection, and cost anomaly alerting for AI/ML workloads using Amazon CloudWatch, Splunk, and FinOps spend control frameworks. - Collaborate with Data Engineering, AI Architects, and Cloud Teams to integrate vector storage/retrieval (RAG), Apache Spark/EMR-on-EKS runtimes, and local tokenization databases. - Author technical MLOps runbooks, model deployment procedures, governance documentation, and disaster recovery playbooks. ###### Requirements: - 4+ years of hands-on experience in MLOps, DataOps, or Platform Engineering with a primary focus on enterprise Amazon SageMaker (Pipelines, Feature Store, Model Registry, Endpoints). - Proven experience deploying and operating Generative AI, LLM/SLM models, and Amazon Bedrock services alongside agentic frameworks and RAG pipelines. - Hands-on expertise with MLflow for experiment tracking, model registry, and lifecycle management. - Solid experience in GPU optimization and orchestration (NVIDIA A100/L40S, AWS EC2 GPU instances) for model training, fine-tuning, and low-latency real-time inference (STT/TTS voice pipelines). - Proficient in building CI/CD for Machine Learning (GitLab CI/CD, GitHub Actions) and Infrastructure-as-Code (Terraform or AWS CDK). - Practical knowledge of LLMOps / AgentOps tools and methodologies (AgentCore, prompt evaluations, Bedrock Guardrails, vector databases for RAG). - Strong understanding of data security, privacy, and tokenization (FPE, handling sensitive/PII data within ML pipelines). - Proficient in Python, PySpark, Docker, and Kubernetes/EKS fundamentals for containerized ML workloads. ###### Nice-to-Have Skills: - AWS Certified Machine Learning – Specialty certification. - AWS Certified Solutions Architect – Associate/Professional or AWS Certified DevOps Engineer – Professional. - Experience in telecom domain AI/ML applications, low-latency real-time voice/chat processing (ASR/TTS), or hybrid cloud data sovereignty architectures. - Experience with EMR-on-EKS, Starburst/Athena, or Apache Iceberg data lake integrations. ###### Soft Skills & Team Fit: - Strong critical thinking, problem-solving, and analytical skills. - Excellent communication and collaboration skills to work closely with cross-functional teams (Data Engineering, AI/GenAI Engineers, Security, Cloud/Infrastructure). - Results-oriented, proactive mindset with strong ownership of deliverables within an Agile / Scrum framework. - Upper-Intermediate+ English level (written and spoken). ###### What we propose: - Opportunity to lead critical, high-impact Data & AI platform delivery for a major telecommunications operator. - Hands-on work with modern MLOps and GenAI stack (Amazon SageMaker, Amazon Bedrock, MLflow, AgentCore, STT/TTS voicebot pipelines). - Flexible remote work options with structured, predictable collaboration within a well-balanced team. **We offer*:** - Flexible working format - remote, office-based or flexible - A competitive salary and good compensation package - Personalized career growth - Professional development tools (mentorship program, tech talks and trainings, centers of excellence, and more) - Active tech communities with regular knowledge sharing - Education reimbursement - Memorable anniversary presents - Corporate events and team buildings - Other location-specific benefits *not applicable for freelancers --- Source: N-iX's own career page, read by RemJobs. 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