Senior Devops Engineer

2 days ago


Ho Chi Minh City, Ho Chi Minh, Vietnam MoMo (M_Service) Full time

We are seeking a
Senior AI Engineer (AI-Ops / MLOps)
to take a leading role in
designing, deploying, and optimizing large-scale AI systems
at MoMo. You will be responsible for
building robust, production-grade infrastructure
that enables fast iteration and reliable serving of
LLMs
, and
speech models

In this position, you will lead
AI deployment strategy
, mentor junior engineers, and collaborate with AI researchers to bridge the gap between
prototyping and scalable production
— ensuring that MoMo's AI innovations reach millions of users efficiently and safely.

Desciption

  • Utilize
    end-to-end
    AI infrastructure pipelines
    on top of existing MoMo infrastructure, from training to serving and continuous monitoring.
  • Lead the
    design and implementation
    of high-throughput
    model serving systems
    (e.g., vLLM, Triton, TensorRT, ONNX Runtime) and
    model serving techniques
    (in-flight batching, speculative inference)
  • Define model-level monitoring and alerting hooks integrated with MoMo's existing observability stack (Prometheus, Grafana).
  • Collaborate with DevOps/CloudOps teams,
    AI research
    ,
    data engineering
    , and
    backend
    teams to ensure smooth integration of AI models into MoMo's ecosystem.
  • Conduct
    performance benchmarking
    and
    capacity planning
    to maintain SLA compliance across AI services.
  • Focus on
    inference optimization
    strategies (quantization, batching, caching, pruning, GPU/TPU resource tuning).
  • Mentor and guide the AI-Ops/MLOps team on
    best practices
    in distributed serving, orchestration, and infrastructure reliability.
  • Evaluate new technologies and tools to improve
    scalability, cost efficiency, and model lifecycle automation
    .

Requirement

Qualifications

  • Bachelor's in
    Computer Science, Artificial Intelligence, Software Engineering
    , or a related discipline.
  • 3+ years of experience in
    AI infrastructure
    ,
    DevOps
    , or
    MLOps
    roles (with at least 1 years at a senior).
  • Familiar with container orchestration (Kubernetes, Docker) and
    infrastructure-as-code
    (Terraform, Helm).
  • Strong proficiency with
    Python
    and
    Bash scripting
    ; experience with
    Go
    or
    Rust
    preferred.
  • Proven experience deploying
    deep learning models
    (e.g., PyTorch, TensorFlow, JAX) at production scale.
  • Hands-on experience with
    CI/CD systems
    (Jenkins, Argo CD, GitHub Actions) and
    monitoring stacks
    (Prometheus, Grafana, ELK).
  • Strong understanding of
    distributed systems
    ,
    load balancing
    , and
    high-availability architectures
    .
  • Demonstrated ability to collaborate with researchers and translate prototypes into
    scalable, reliable production systems
    .

Nice to Have

  • Experience with LLM inference optimization (vLLM, LoRA adapters, quantization, multi-GPU scheduling).
  • Familiarity with speech or vision models and multi-modal AI pipelines.
  • Experience integrating LLM and speech models into scalable inference microservices.
  • Knowledge of feature stores, data versioning, and real-time feature serving (Feast, Redis, Kafka).
  • Background in security, privacy, or governance frameworks for AI systems.


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