Senior MLOps Engineer

Work type:

Office/Remote

Technical Level:

Senior

Job Category:

Software Development

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.

• Build, operationalize, and automate end-to-end MLOps pipelines using Amazon Sage Maker Pipelines and MLflow for experiment tracking, model versioning, and registry lifecycle management.
• Design, deploy, and manage production Sage Maker 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 Agent Ops / LLMOps frameworks (Agent Core, 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/SLM model training, fine-tuning, and scalable inference workloads.
• Establish automated CI/CD for Machine Learning using Git Lab CI/CD pipelines and Infrastructure-as-Code (
  Terraform or AWS CDK
  ) to enforce security-gated MLOps promotion workflows (from Sage Maker 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 Cloud Watch
  , Splunk
  , and Fin Ops 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.

• 4+ years of hands-on experience in MLOps, Data Ops, or Platform Engineering with a primary focus on enterprise Amazon Sage Maker (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 (Git Lab CI/CD, Git Hub Actions) and Infrastructure-as-Code (
  Terrafo…