Verified Job
Engineering - Architecture
Senior MLOps Engineer-(Gen AI+LLM+Terraform+Kubernetes)-Remote Opportunity
Engineering - Architecture
#50698
Remote / WFH
Spectral Consultants
Job Description
Spectral Consultants is hiring Senior MLOps Engineer (GenAI / LLM) with one of the leading US-based product organizations.
📍 Location: Noida (Remote)
💼 Experience: 7–10 years
Role Overview:-
The ideal candidate will be responsible for building and scaling production-ready ML and Generative AI (LLM) systems on AWS, enabling seamless deployment, monitoring, and optimization of AI models.
Key Responsibilities:-
• Design, build, and scale ML/AI platforms on AWS using services such as SageMaker, Bedrock, S3, Lambda, ECS, and 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 such as CloudWatch, Prometheus, and Grafana.
• Work with vector databases such as OpenSearch, Pinecone, and FAISS.
• Collaborate with Data Scientists, ML Engineers, and cross-functional teams to productionize ML models.
• Ensure platform security, governance, and cost optimization aligned with industry best practices.
Candidate Requirements:-
• 7–10 years of experience in MLOps, ML Engineering, or related domains.
• Strong hands-on experience with Generative AI, LLMs, and RAG pipelines.
• Expertise in the AWS ecosystem, including SageMaker, Bedrock, S3, Lambda, ECS, and 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 such as OpenSearch, Pinecone, and FAISS.
• Solid understanding of CI/CD pipelines for ML systems.
• Strong problem-solving, communication, and collaboration skills.
📍 Location: Noida (Remote)
💼 Experience: 7–10 years
Role Overview:-
The ideal candidate will be responsible for building and scaling production-ready ML and Generative AI (LLM) systems on AWS, enabling seamless deployment, monitoring, and optimization of AI models.
Key Responsibilities:-
• Design, build, and scale ML/AI platforms on AWS using services such as SageMaker, Bedrock, S3, Lambda, ECS, and 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 such as CloudWatch, Prometheus, and Grafana.
• Work with vector databases such as OpenSearch, Pinecone, and FAISS.
• Collaborate with Data Scientists, ML Engineers, and cross-functional teams to productionize ML models.
• Ensure platform security, governance, and cost optimization aligned with industry best practices.
Candidate Requirements:-
• 7–10 years of experience in MLOps, ML Engineering, or related domains.
• Strong hands-on experience with Generative AI, LLMs, and RAG pipelines.
• Expertise in the AWS ecosystem, including SageMaker, Bedrock, S3, Lambda, ECS, and 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 such as OpenSearch, Pinecone, and FAISS.
• Solid understanding of CI/CD pipelines for ML systems.
• Strong problem-solving, communication, and collaboration skills.
