Expert Reinforcement Learning Specialist

1 week ago

Cochin, Kerala, India beBeeMachineLearning Full time ₹ 15,00,000 - ₹ 20,00,000

AWS MLOps & LLMOps Engineer Role

Job Overview:

We are seeking a skilled and customer-facing engineer with expertise in reinforcement learning, SageMaker, Feature Store, and continuous ML lifecycle management. You'll lead the deployment and monitoring of ML and LLM models, collaborate with data scientists to operationalise ML and LLM models, including fine-tuning and prompt engineering, and take full ownership of AI projects from ideation to production.

Key Responsibilities:

  • Design and implement scalable 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 operationalise 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

Required Skills and 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
  • Bachelor's or master's 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 Speciality 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 management

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