AWS services - Bedrock, SageMaker
Posted 1 day ago by 1751528602
Negotiable
Outside
Remote
USA
Summary: This role focuses on leveraging AWS services such as Bedrock and SageMaker to implement machine learning and artificial intelligence solutions. The candidate will be responsible for developing cloud-native applications and managing the software release lifecycle while collaborating with stakeholders to meet business objectives. A strong background in Python and experience with large language models and Infrastructure as Code practices are essential. The position is remote and classified as outside IR35.
Key Responsibilities:
- Hands-on role using AWS (Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, S3).
- Responsible for the implementation of AWS cloud services including infrastructure, machine learning, and artificial intelligence platform services.
- Experience with LLM-based applications, including Retrieval-Augmented Generation (RAG) using LangChain and other frameworks.
- Develop cloud-native microservices, APIs, and serverless functions to support intelligent automation and real-time data processing.
- Collaborate with internal stakeholders to understand business goals and translate them into secure, scalable AI systems.
- Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code (Terraform).
- Support the development and evolution of reusable platform components for AI/ML operations.
- Create and maintain technical documentation for the team to reference and share with our internal customers.
- Excellent verbal and written communication skills in English.
Key Skills:
- 7 years of hands-on software engineering experience with a strong focus on Python.
- Experienced with AWS services, especially Bedrock or SageMaker.
- Familiar with fine-tuning large language models or building datasets and/or deploying ML models to production.
- Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config).
- Solid experience implementing RAG architectures and LangChain.
- Demonstrated experience in Infrastructure as Code best practices and experience with building Terraform modules for AWS cloud.
- Strong background in Git-based version control, code reviews, and DevOps workflows.
- Demonstrated success delivering production-ready software with release pipeline integration.
Salary (Rate): undetermined
City: undetermined
Country: USA
Working Arrangements: remote
IR35 Status: outside IR35
Seniority Level: undetermined
Industry: IT
Must Have:
AWS services- Bedrock, SageMaker, ECS and Lambda
Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config)
Experience implementing RAG architectures and using frameworks and ML tooling like: Transformers, PyTorch, TensorFlow, and LangChain
Experience in Infrastructure as Code best practices and experience with building Terraform modules for AWS cloud
Fine-tuning large language models, building datasets and deploying ML models to production
Git-based version control, code reviews, and DevOps workflows
Nice To Have:
AWS or relevant cloud certifications
Data privacy and compliance best practices (e.g., PII handling, secure model deployment)
Data science background or experience working with structured/unstructured data
Exposure to FinOps and cloud cost optimization
Hugging Face, Node.js
Policy as Code development (I.e. Terraform Sentinel)
DUTIES AND RESPONSIBILITIES:
Hands-on role using AWS (Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, S3).
Responsible for the implementation of AWS cloud services including infrastructure, machine learning, and artificial intelligence platform services.
Experience with LLM-based applications, including Retrieval-Augmented Generation (RAG) using LangChain and other frameworks.
Develop cloud-native microservices, APIs, and serverless functions to support intelligent automation and real-time data processing.
Collaborate with internal stakeholders to understand business goals and translate them into secure, scalable AI systems.
Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code (Terraform).
Support the development and evolution of reusable platform components for AI/ML operations.
Create and maintain technical documentation for the team to reference and share with our internal customers.
Excellent verbal and written communication skills in English.
MINIMUM KNOWLEDGE, SKILLS, AND ABILITIES REQUIRED:
7 years of hands-on software engineering experience with a strong focus on Python.
Experienced with AWS services, especially Bedrock or SageMaker
Familiar with fine-tuning large language models or building datasets and/or deploying ML models to production.
Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config).
Solid experience implementing RAG architectures and LangChain.
Demonstrated experience in Infrastructure as Code best practices and experience with building Terraform modules for AWS cloud.
Strong background in Git-based version control, code reviews, and DevOps workflows.
Demonstrated success delivering production-ready software with release pipeline integration.
Nice-to-Haves:
AWS or relevant cloud certifications.
Policy as Code development (e.g., Terraform Sentinel).
Experience with Hugging Face, Golang, or Node.js.
Exposure to FinOps and cloud cost optimization.
Data science background or experience working with structured/unstructured data.
Awareness of data privacy and compliance best practices (e.g., PII handling, secure model deployment).