Senior MLOps Engineer (#5860)

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.