51 to 75 of 244 Amazon SageMaker Jobs

architecture strategy. Keep us ahead of AWS roadmaps in priority services, and set the technical bar as the alliance scales. Technical Depth Generative AI: Amazon Bedrock, SageMaker, agentic and generative AI application patterns Data & Analytics: Amazon Marketing Cloud (AMC), AWS Clean Rooms, data lakes, analytics pipelines Customer … Experience: Amazon Personalize, Amazon Connect, serverless architectures Modern Applications: Lambda, Step Functions, API Gateway, container orchestration Integration: Event-driven architectures, microservices, API design How Success Looks Technical win rate on the strategic pursuits you lead Competitive deals turned in our favor by your technical credibility Reference architectures adopted ...

deploying ML models at scale using Docker, Kubernetes, or serverless Deep understanding of ML fundamentals: optimisation, regularisation, evaluation metrics Experience with cloud ML services (SageMaker, Vertex AI, or Azure ML) Strong software engineering practices: version control, testing, CI/CD MSc or PhD in Computer Science, Machine Learning ...

Awareness of ethical, privacy, security and policy considerations in applied AI Desirable Experience Hands-on experience with AWS services such as Lambda, ECS, S3, SageMaker or Bedrock Knowledge of ML engineering and MLOps practices (e.g. Docker, MLflow) Experience working in government, defence or other highly regulated environments Proven application ...

Experience in a specific domain relevant to the business (e.g., banking, financial services, insurance). Exposure to MLOps/LLMOps tooling (MLflow, Vertex AI, SageMaker, Azure AI Studio). Experience presenting architecture and AI strategy to senior/executive stakeholders. HCLTech is an equal opportunity employer, committed to providing ...

tools, and raising potential risks to senior colleagues. Basic understanding of cloud platforms (AWS, Azure, or GCP); exposure to cloud-native AI tools (e.g., SageMaker, Vertex AI, Azure ML Studio) is a plus. Awareness of containerisation technologies (e.g., Docker); exposure to orchestration tools (e.g., Kubernetes) is beneficial ...

tools, and raising potential risks to senior colleagues. Basic understanding of cloud platforms (AWS, Azure, or GCP); exposure to cloud-native AI tools (e.g., SageMaker, Vertex AI, Azure ML Studio) is a plus. Awareness of containerisation technologies (e.g., Docker); exposure to orchestration tools (e.g., Kubernetes) is beneficial ...

. Hands‐on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud‐native AI/ML platforms (e.g. AWS SageMaker, AzureML) and agent orchestration frameworks (e.g. LangChain, LangGraph, ADK). Solid understanding of MLOps – CI/CD, IaC (Terraform), experiment tracking, model registry ...

monitoring.Experience creating automated evaluations for generative AI applications.Experience with AWS, Docker, Kubernetes, Terraform, and continuous integration and deployment practices.Experience using services such as AWS SageMaker or AWS Bedrock.Experience with Kafka, vector databases, or other technologies used for real time and high dimensional data processing.Experience managing machine learning workflows using ...

frameworks (PyTorch, TensorFlow). Prompt Engineering, Langchain,Langsmith/Langfuse, Gen AI/Agentic AI , Cloud platforms (Azure AI Foundry, AWS Bedrock/Sagemaker, GCP Vertex AI) , MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes).Preferred•Delivered AI projects within Agile frameworks•Experience on Gen AI Feedback Analysis, topic ...

deploy machine learning models and statistical analyses in production environments Architect and manage AWS cloud infrastructure for data workloads (S3, Lambda, Glue, Athena, SageMaker, Step Functions) Collaborate with domain experts to translate rail inspection requirements into technical solutions Write clean, tested, well-documented code following engineering best practices Participate ...

production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). Software Engineering: Production-grade Python services (FastAPI, AWS Lambda, event-driven patterns) and front-end development with React/Next.js ...

production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn, PyTorch, XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). Software Engineering: Production-grade Python services (FastAPI, AWS Lambda, event-driven patterns) and front-end development with React/Next.js ...

Retrieval-Augmented Generation (RAG) AI Agents and Agentic AI Multi-agent systems Voice AI Speech-to-text/text-to-speech ElevenLabs AWS Bedrock Amazon SageMaker Google Cloud Platform (GCP) Vertex AI Python FastAPI/APIs LangChain/LangGraph Vector databases Embeddings and semantic search Hugging Face/ ...

with Cloud services, Infrastructure as Code (IaC) and containerized application development Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and Large Language Model Operations (LLMOps) Experience working on AIML systems and/ ...

including Docker and/or Podman . Practical cloud experience across one or more of AWS, Azure or GCP , ideally using services such as SageMaker, Azure ML or Vertex AI. Experience with CI/CD and MLOps tooling such as GitLab CI, GitHub Actions, Jenkins, Kubeflow or MLflow . ...

robust scientific workAwareness of ethical, privacy, security and policy considerations in applied AIDesirable ExperienceHands-on experience with AWS services such as Lambda, ECS, S3, SageMaker or BedrockKnowledge of ML engineering and MLOps practices (e.g. Docker, MLflow)Experience working in government, defence or other highly regulated environmentsProven application of large ...

deploy machine learning models and statistical analyses in production environments Architect and manage AWS cloud infrastructure for data workloads (S3, Lambda, Glue, Athena, SageMaker, Step Functions) Collaborate with domain experts to translate rail inspection requirements into technical solutions Write clean, tested, well-documented code following engineering best practices Participate ...

Kubernetes).Expertise with modern data platforms (e.g., BigQuery/Databricks) and data processing workflows (ETL, pipelines).Excellent experience with cloud hosted AI platforms (Bedrock, Sagemaker, VertexAI)Strong problem-solving abilities, with the capacity to learn quickly and adapt in a fast-paced environment.Excellent communication and a drive to work ...

Expertise with modern data platforms (e.g., BigQuery/Databricks) and data processing workflows (ETL, pipelines). Excellent experience with cloud hosted AI platforms (Bedrock, Sagemaker, VertexAI) Strong problem-solving abilities, with the capacity to learn quickly and adapt in a fast-paced environment. Excellent communication and a drive ...

Kubernetes).Expertise with modern data platforms (e.g., BigQuery/Databricks) and data processing workflows (ETL, pipelines).Excellent experience with cloud hosted AI platforms (Bedrock, Sagemaker, VertexAI)Strong problem-solving abilities, with the capacity to learn quickly and adapt in a fast-paced environment. Excellent communication and a drive ...

scikit, pytorch, tidyverse, caret, ggplot, etc.).Proven experience developing, refining, and monitoring NLP models.Familiarity with database management tools and techniques (e.g., SQL, Selenium, S3, Sagemaker, API protocols) is preferred but not required.Understanding model evaluation methods and metrics.Ability to operationalize non-technical ideas into relevant research designs, features, and model ...

everything that gets those models in front of a customer and keeps them running: the training and deployment infrastructure on AWS and SageMaker, reproducible and configuration-driven training runs, environments defined in code, release pipelines that make a deployment routine rather than an event, and the monitoring that tells … machine learning engineers/scientists and turn them into production services that meet our latency, cost and reliability targets. Build and maintain SageMaker training, processing and inference workloads, and the pipelines that orchestrate them. Make training runs reproducible and configuration-driven, so any result can be rebuilt from code. ...

equivalents in Azure or GCP), with hands‐on work across classical ML and modern LLM/RAG systems using services like Amazon SageMaker and Bedrock. What you’ll doPartner with stakeholders across the business to explore high‐impact opportunities.Own the full lifecycle: problem framing, data discovery, feature engineering … methods, and building RAG systems.Orchestration: for example LangChain for pipelines/agents. RAG best practices and evaluation workflows (e.g., agentic/RAG patterns on SageMaker). Comfortable choosing the right technique for the job (from baselines to advanced models), with an emphasis on measurable impact and maintainability.Clear communication with ...

APIs, API Gateway, and Spring Boot for application integrations AI/ML Integration: Implementing AI/ML integration layers connecting applications to services like Amazon Bedrock, SageMaker, and OpenAI APIs for intelligent features Event-Driven Architecture: Building event-driven architectures using Kafka/MSK, AWS EventBridge … work status. We are unfortunately unable to offer sponsorship Desirable AWS serverless services: Lambda, API Gateway, DynamoDB, EventBridge, Step Functions AI/ML platforms: Amazon Bedrock, SageMaker, OpenAI APIs, or similar services Event-driven architecture: Messaging systems and asynchronous processing (Kafka/SQS) Infrastructure as Code: CloudFormation ...

Turn machine learning engineer/scientist models into production services meeting latency, cost, and reliability targets • Build and maintain SageMaker training, processing, and inference workloads • Build pipelines that orchestrate SageMaker workloads • Make training runs reproducible and configuration-driven so results can be rebuilt from code • Build monitoring … Deep hands-on PyTorch experience; TensorFlow welcome Experience with Hugging Face Transformers, timm, scikit-learn, NumPy, pandas, and OpenCV Production experience on AWS, specifically Amazon SageMaker, including training and processing jobs, pipelines, model registry, and endpoints Practical MLOps experience with MLflow, hyperparameter optimisation, and reproducible, configuration-driven training ...