Senior MLOps Engineer

About the Position

We are seeking a Senior MLOps Engineer to lead critical Data & AI platform delivery for a major telecommunications operator, focusing on modern MLOps and GenAI stack.

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

• 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.

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

About the Company

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.