MLOPS Engineer

Job Ove-rview

hands-on and customer-facing AWS MLOps & LLMOps Engineer

Key Responsibilities

• Design and implement

  MLOps and LLMOps pipelines

  using AWS services (SageMaker Pipelines, Lambda, EKS, ECS, etc.)
• Build and manage

  reinforcement learning pipelines

  , including simulation environments, reward modeling, and policy optimization
• Integrate and maintain

  Amazon SageMaker Feature Store

  for real-time and batch feature ingestion
• Enable

  continuous training

  ,

  model monitoring

  , and

  automated deployment

  using CI/CD workflows
• Collaborate with data scientists to operationalize ML and LLM models, including fine-tuning and prompt engineering
• Develop and maintain

  High-Level Designs (HLD)

  and

  network architecture

  for scalable AI solutions
• Engage with customers to understand requirements and propose tailored AI/ML solutions
• Provide

  technical support for RFPs

  , including architecture design, effort estimation, and documentation
• Ensure

  security, compliance, and governance

  across all ML/LLM workflows
• Lead and support

  AI projects

  with full ownership from experimentation to production
• Create architecture HLD with networking, data flow, components and integration diagram

Required Skills & Qualifications

• 2-6 years of experience in

  MLOps

  , with hands-on exposure to

  LLMOps

  and

  reinforcement learning

• Strong experience with

  AWS SageMaker

  , including Pipelines, Model Registry, and Feature Store
• Proficiency in

  Python

  ,

  Docker

  ,

  Terraform

  , and

  CI/CD tools

• Familiarity with

  RL frameworks

  like Ray RLlib, OpenAI Gym, or Stable Baselines
• Experience with

  ML frameworks

  such as TensorFlow, PyTorch, and Hugging Face Transformers
• Solid understanding of

  networking

  ,

  security protocols

  , and

  cloud-native architecture

• Excellent communication and

  client engagement skills

• Bachelors or masters degree in computer science, Data Science, or related field

Preferred Qualifications

• Good to have experience with

  Kubernetes

  (EKS preferred) for container orchestration and scalable ML deployments
• AWS Certified Machine Learning Specialty or equivalent certifications
• Exposure to

  model monitoring tools

  (e.g., SageMaker Model Monitor, Prometheus, Grafana)
• Knowledge of

  LLM evaluation

  ,

  bias detection

  , and

  hallucination mitigation

• Familiarity with

  data lake architectures

  ,

  feature engineering

  , and

  metadata managementRole & responsibilities

Preferred candidate profile