AWS MLOps
3 weeks ago
Location: Kochi
Employment Type: Full-Time
Job Overview
We are seeking a hands-on and customer-facing AWS MLOps & LLMOps 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, support sales with RFPs, and take full ownership of AI projects from ideation to production. Experience with Kubernetes is a strong plus.
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 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
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
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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