AWSCloudEC2JavaScriptMicroservicesPythonSQLTerraformBashAIMachine LearningMLNLPNatural Language ProcessingGenerative AILLMOpenAIClaudeRAGLangChainHugging FaceAnalyticsCloudFormationLambdaS3RDSSageMakerBedrockAPI GatewayGitVersion ControlCI/CDCommunicationPenetration Testing
About this role
Role Overview
Design, develop, and deploy AI and machine learning models to solve real business problems and automate workflows
Build and maintain end-to-end ML pipelines from data ingestion and preprocessing through model training, evaluation, and production deployment
Develop natural language processing (NLP), large language model (LLM) integrations, and generative AI solutions as applicable
Fine-tune and optimize pre-trained models (including GPT, Claude, or open-source alternatives) for specific use cases
Evaluate model performance, monitor for drift, and implement improvements based on real-world feedback
Research and apply emerging AI techniques, frameworks, and tools to continuously improve solution quality
Integrate AI models and APIs into existing applications, platforms, and workflows
Build intelligent automation solutions that reduce manual effort and improve operational throughput
Develop AI-powered features including chatbots, recommendation engines, document processing, and predictive analytics
Design and implement RAG (Retrieval-Augmented Generation) architectures for knowledge-based AI applications
Collaborate with product and operations teams to identify high-value AI use cases and deliver solutions
Write clean, well-documented, production-quality code primarily in Python, with additional languages as needed (JavaScript, SQL, Bash, etc.)
Build APIs, microservices, and data pipelines that support AI workloads at scale
Apply software engineering best practices including version control (Git), code review, testing, and CI/CD
Maintain and refactor existing codebases for performance, reliability, and maintainability
Document technical architectures, implementation decisions, and system behaviors clearly
Architect, deploy, and manage AI and data workloads on AWS cloud infrastructure
Utilize AWS services including SageMaker, Lambda, EC2, S3, RDS, Bedrock, Step Functions, and API Gateway
Build scalable, cost-efficient cloud architectures that support model training, inference, and data processing
Implement infrastructure-as-code using AWS CloudFormation, CDK, or Terraform
Monitor cloud resource utilization and optimize for performance and cost
Ensure all AWS environments are configured in alignment with security and compliance requirements
Design and develop all AI systems and data pipelines in full compliance with HIPAA Privacy and Security Rules
Ensure Protected Health Information (PHI) is handled, stored, transmitted, and processed with appropriate safeguards
Implement technical controls including encryption at rest and in transit, access controls, and audit logging for all PHI-adjacent systems
Participate in HIPAA risk assessments and support remediation of identified vulnerabilities
Maintain documentation required for HIPAA compliance including data flow diagrams, system inventories, and access logs
Stay current on HIPAA regulatory developments and ensure AI systems remain compliant as regulations evolve
Build and maintain AI systems and cloud infrastructure in accordance with SOC 2 Trust Service Criteria (Security, Availability, Confidentiality, Processing Integrity, and Privacy)
Implement and maintain security controls required for SOC 2 Type I and Type II certification
Support audit preparation by maintaining evidence, access logs, and system documentation
Participate in vulnerability management, penetration testing, and incident response processes
Collaborate with security and compliance teams to ensure all AI deployments meet SOC 2 standards
Monitor systems continuously for security events and compliance gaps
Design secure data architectures that protect sensitive information throughout the AI pipeline
Implement role-based access controls, data masking, and anonymization techniques where appropriate
Ensure data governance practices are followed for all datasets used in model training and inference
Maintain data lineage documentation and audit trails for compliance and reproducibility
Work closely with engineering, product, operations, and compliance teams to align AI solutions with business needs and regulatory requirements
Communicate complex technical concepts clearly to non-technical stakeholders
Produce thorough technical documentation for all systems, models, and integrations
Mentor junior team members on AI development practices and compliance standards
Requirements
3 or more years of hands-on experience in AI, machine learning, or data science engineering roles
Strong programming skills in Python — this is the primary development language for this role
Demonstrated experience building and deploying ML models or AI-powered applications in production environments
Proficiency with AWS cloud services — particularly those relevant to AI/ML workloads (SageMaker, Lambda, S3, EC2, Bedrock, or equivalent)
Working knowledge of HIPAA requirements and experience building systems that handle PHI in compliance with applicable regulations
Familiarity with SOC 2 compliance frameworks and the technical controls required to support certification
Experience with LLMs, NLP, or generative AI frameworks such as LangChain, OpenAI API, Hugging Face, or similar
Strong understanding of data security, encryption, access control, and audit logging best practices
Excellent written and verbal communication skills, including the ability to document technical work clearly.