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.
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.
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).
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.

Senior MLOps Engineer

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