4 of 4 Contract Amazon SageMaker Jobs

agent solutions that integrate with enterprise systems and cloud platforms. Develop and optimize RESTful and GraphQL APIs to facilitate AI-driven interactions. Utilize AWS services (Lambda, S3, API Gateway, SageMaker, DynamoDB, ECS, etc.) to deploy scalable AI solutions. Implement Full Stack JavaScript (Node.js, React.js, Express, TypeScript, Next.js, etc.) applications to support AI-driven interfaces. Work closely with data scientists More ❯

and development to succeed. A progressive organisation where you can really make a difference. We a great opportunity for a Data Scientist. You will have solid experience of Bedrock, Sagemaker, Python and Pandas. Key Responsibilities: * Working experience LLMs under Bedrock and Sagemaker both Amazon * Python with APIs to ChatGPT * Strong coding skills using libraries like pandas, NumPy … Experience: AWS Data Science Environment: Hands-on experience with Sage Maker, Lambda, Step Functions, S3, Athena. Model deployment and pipeline orchestration in AWS. OCR Use-Case Development: Proficiency with Amazon Tex-tract, Tesseract, and LLM-based OCR. Building document parsing pipelines, validations, and rule Python Proficiency: Strong coding skills using libraries like pandas, NumPy, scikit-learn, PyTorch, and Hugging … actionable insights. Stakeholder Collaboration: Effective communication with technical and non-technical stakeholders. Experience in cross-functional team. Data Engineering Basics: Familiarity with SQL and big data processing tools (e.g., Amazon Athena). Model Evaluation & Experimentation: A/B testing, statistical analysis, and performance metrics. Security & Compliance Awareness: Understanding of data privacy, PII handling, and compliance standards (e.g., GDPR). More ❯

balance cost, speed and data quality. Experimentation - set up offline metrics and online A/B tests; analyse uplift and iterate quickly. Production delivery - build scalable pipelines in AWS SageMaker (moving to Azure ML); containerise code and hook into CI/CD. Monitoring & tuning - track drift, response quality and spend; implement automated retraining triggers. Collaboration - work with Data Engineering … Data Factory) job. Review: inspect dashboards, compare control vs. treatment, plan next experiment. Tech stack Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow)SQL (Redshift, Snowflake or similar)AWS SageMaker Azure ML migration, with Docker, Git, Terraform, Airflow/ADFOptional extras: Spark, Databricks, Kubernetes. What you'll bring 3-5+ years building optimisation or recommendation systems at scale. More ❯

organisation. . Experience of defining, building and maintaining infrastructure-as-code using Terraform. . Awareness of AI/ML model development, and how to build and train them (using Sagemaker) in cloud-based solutions. . Significant experience of the full life cycle of a cloud-based platforms and applications from designing, through deployment to supporting and patching (in particular More ❯