Senior MLOps Engineer

You will be responsible for building and scaling production-ready ML & GenAI (LLM) systems on AWS, enabling seamless deployment, monitoring, and optimization of AI models. Key Responsibilities: - Design, build, and scale ML/AI platforms on AWS, leveraging services such as SageMaker, Bedrock, S3, Lambda, ECS/EKS. - Develop and deploy LLM-based applications, including RAG pipelines, embeddings, and vector search solutions. - Build and manage end-to-end ML pipelines (data ingestion training deployment monitoring). - Implement CI/CD pipelines for ML and LLM systems, ensuring automated testing, validation, and deployment. - Deploy and manage real-time and batch inference systems with high scalability and reliability. - Monitor model performance, drift, and bias using tools like CloudWatch, Prometheus, and Grafana. - Work with vector databases such as OpenSearch, Pinecone, or FAISS. - Collaborate with Data Scientists, ML Engineers, and cross-functional teams to productionize ML models. - Ensure platform security, governance, and cost optimization aligned with best practices. Candidate Requirements: - 10+ years of experience in ML Engineering, MLOps, or related domains. - Strong hands-on experience with Generative AI, LLMs, and RAG pipelines. - Expertise in AWS ecosystem, including SageMaker, Bedrock, S3, Lambda, ECS/EKS. - Experience with model deployment, monitoring, and ML lifecycle management. - Strong programming skills in Python and experience with APIs (FastAPI/Flask). - Hands-on experience with Docker, Kubernetes, and Infrastructure-as-Code (Terraform/CloudFormation). - Experience working with vector databases (OpenSearch, Pinecone, FAISS). - Solid understanding of CI/CD pipelines for ML systems. - Strong problem-solving, communication, and collaboration skills. You will be responsible for building and scaling production-ready ML & GenAI (LLM) systems on AWS, enabling seamless deployment, monitoring, and optimization of AI models. Key Responsibilities: - Design, build, and scale ML/AI platforms on AWS, leveraging services such as SageMaker, Bedrock, S3, Lambda, ECS/EKS. - Develop and deploy LLM-based applications, including RAG pipelines, embeddings, and vector search solutions. - Build and manage end-to-end ML pipelines (data ingestion training deployment monitoring). - Implement CI/CD pipelines for ML and LLM systems, ensuring automated testing, validation, and deployment. - Deploy and manage real-time and batch inference systems with high scalability and reliability. - Monitor model performance, drift, and bias using tools like CloudWatch, Prometheus, and Grafana. - Work with vector databases such as OpenSearch, Pinecone, or FAISS. - Collaborate with Data Scientists, ML Engineers, and cross-functional teams to productionize ML models. - Ensure platform security, governance, and cost optimization aligned with best practices. Candidate Requirements: - 10+ years of experience in ML Engineering, MLOps, or related domains. - Strong hands-on experience with Generative AI, LLMs, and RAG pipelines. - Expertise in AWS ecosystem, including SageMaker, Bedrock, S3, Lambda, ECS/EKS. - Experience with model deployment, monitoring, and ML lifecycle management. - Strong programming skills in Python and experience with APIs (FastAPI/Flask). - Hands-on experience with Docker, Kubernetes, and Infrastructure-as-Code (Terraform/CloudFormation). - Experience working with vector databases (OpenSearch, Pinecone, FAISS). - Solid understanding of CI/CD pipelines for ML systems. - Strong problem-solving, communication, and collaboration skills.