DevOps & MLOps Engineer

4 weeks ago

Canada AddSource Full time

DevOps & MLOps Engineer

Canada (Remote) - Work in EST hours

Experience:

  • 6 - 10 years of experience

We are seeking a highly skilled DevOps & MLOps Engineer with 5+ years of experience to architect, deploy, and optimize the infrastructure for our commercial Generative AI product . This role is ideal for professionals who excel at the intersection of DevOps, MLOps, and AI infrastructure , ensuring secure, scalable, and cost-efficient LLM deployments.

You will work with cutting-edge technologies such as LLMs, vector databases, Databricks, and GPU scaling , contributing to the fine-tuning and large-scale deployment of AI models.

Key Responsibilities

1. Cloud & Hybrid Infrastructure Management

  • Architect and maintain secure, scalable cloud infrastructure on AWS (preferred), GCP, or hybrid-cloud setups.
  • Deploy GPU-accelerated compute clusters on AWS for cost-efficient model training and inference .
  • Implement best practices for VPC networking, IAM security, encryption, and access controls .

2. MLOps & Model Deployment

  • Build and maintain end-to-end MLOps pipelines for LLM training, fine-tuning, and inference .
  • Optimize GPU utilization, autoscaling, and resource allocation for large-scale LLM workloads .
  • Integrate Databricks & MLflow for scalable model training and tracking .
  • Deploy models with TorchServe, Triton, vLLM, or Ray Serve for efficient inference.

3. CI/CD & Automation

  • Develop CI/CD pipelines for model versioning, API services, and infrastructure automation using Terraform and GitHub Actions .
  • Automate model deployment & rollback strategies for reliable AI system updates .

4. Observability, Performance Tuning & Cost Optimization

  • Implement monitoring & logging tools (Prometheus, Grafana, CloudWatch) for LLM performance tracking .

5. Vector Databases & Retrieval-Augmented Generation (RAG)

  • Deploy and optimize vector databases (Pinecone, FAISS, Weaviate, ChromaDB ) for RAG-based LLMs .
  • Improve search and retrieval efficiency to enhance AI model responses .

6. Security & Compliance

  • Ensure secure AI model deployments with role-based access, encryption, and cloud security best practices .
  • Comply with GDPR, SOC 2, and enterprise AI security requirements .

Required Qualifications

  • 5+ years of experience in DevOps, MLOps, or AI Infrastructure Engineering .
  • Strong expertise in AWS (preferred), GCP, or hybrid cloud deployments .
  • Hands-on experience with deploying and scaling LLMs in production .
  • Proficiency in Databricks, MLflow, and Spark-based ML workflows .
  • Strong knowledge of Kubernetes, Docker, Terraform, and CI/CD tools .
  • Experience with GPU scaling, model quantization, and inference acceleration .
  • Familiarity with LLM model serving (AWS SageMaker, BedRock).
  • Expertise in vector databases (Pinecone, FAISS, Weaviate, ChromaDB) for RAG workflows .
  • Solid understanding of network security, IAM, and encryption .

Nice-to-Have Skills

  • Experience with multi-cloud deployments & on-prem AI infrastructure .
  • Familiarity with fine-tuning LLMs using LoRA, DeepSpeed, or Hugging Face .
  • Exposure to AI cost optimization strategies (Spot Instances, Serverless AI, GPU scheduling).
  • Knowledge of LLM observability tools (WhyLabs, Arize AI, LangSmith).

Experience:

  • 6 - 10 years of experience

Location:

  • Remote (EST hours) – Only Canadian candidates will be considered.

Educational Qualifications:

  • Engineering Degree – BE/ME/BTech/MTech/BSc/MSc .
  • Technical certifications in multiple technologies are desirable.

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