AI & Cloud Engineering

This is a fully remote position, open to applicants in Kenya.

📋 Description

• Design, develop, and deploy AI and machine learning models to address real-world business challenges and streamline workflows.

• Create and sustain end-to-end ML pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment in production.

• Develop integrations for natural language processing (NLP), large language models (LLM), and generative AI solutions as applicable.

• Fine-tune and enhance pre-trained models (such as GPT, Claude, or open-source alternatives) to cater to specific use cases.

• Assess model performance, track for drift, and implement enhancements based on practical feedback.

• Investigate and apply cutting-edge AI techniques, frameworks, and tools to continually elevate solution quality.

• Integrate AI models and APIs into existing applications, platforms, and workflows.

• Create intelligent automation solutions that minimize manual efforts and boost operational efficiency.

• Develop AI-driven features like chatbots, recommendation systems, document processing, and predictive analytics.

• Design and execute RAG (Retrieval-Augmented Generation) architectures for knowledge-based AI applications.

• Partner with product and operations teams to identify valuable AI use cases and deliver effective solutions.

• Write clean, well-documented, production-ready code primarily in Python, with additional languages as necessary (JavaScript, SQL, Bash, etc.).

• Build APIs, microservices, and data pipelines that support scalable AI workloads.

• Employ software engineering best practices, including version control (Git), code reviews, testing, and CI/CD.

• Maintain and refactor existing codebases to enhance performance, reliability, and maintainability.

• Clearly document technical architectures, implementation choices, and system behaviors.

• Architect, deploy, and manage AI and data workloads on AWS cloud infrastructure.

• Utilize AWS services such as SageMaker, Lambda, EC2, S3, RDS, Bedrock, Step Functions, and API Gateway.

• Create scalable, cost-effective cloud architectures to support model training, inference, and data processing.

• Implement infrastructure-as-code using AWS CloudFormation, CDK, or Terraform.

• Monitor cloud resource utilization and optimize for both performance and cost.

• Ensure all AWS environments are set up in compliance with security and regulatory standards.

• Design and develop all AI systems and data pipelines in full adherence to HIPAA Privacy and Security Rules.

• Safeguard Protected Health Information (PHI) through appropriate handling, storage, transmission, and processing measures.

• Enforce technical controls such as encryption both at rest and in transit, access controls, and audit logging for all PHI-related systems.

• Engage in HIPAA risk assessments and assist in addressing identified vulnerabilities.

• Maintain documentation required for HIPAA compliance, including data flow diagrams, system inventories, and access logs.

• Stay updated on HIPAA regulatory changes to ensure AI systems remain compliant as regulations evolve.

• Build and uphold AI systems and cloud infrastructure in accordance with SOC 2 Trust Service Criteria (Security, Availability, Confidentiality, Processing Integrity, and Privacy).

• Implement and manage security controls necessary 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 that all AI deployments adhere to SOC 2 standards.

• Continuously monitor systems for security events and compliance discrepancies.

• Design secure data architectures that protect sensitive information throughout the AI pipeline.

• Implement role-based access controls, data masking, and anonymization techniques when appropriate.

• Ensure data governance practices are upheld for all datasets utilized 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 objectives and regulatory requirements.

• Communicate intricate technical concepts effectively to non-technical stakeholders.

• Produce comprehensive technical documentation for all systems, models, and integrations.

• Mentor junior team members in AI development methodologies and compliance standards.

⛳️ Requirements

• A minimum of 3 years of practical experience in AI, machine learning, or data science engineering roles.

• Strong programming capabilities in Python, which is the primary development language for this position.

• Proven experience in building and deploying ML models or AI-powered applications in production settings.

• Proficient in AWS cloud services, particularly those relevant to AI/ML workloads (SageMaker, Lambda, S3, EC2, Bedrock, or equivalent).

• Familiarity with HIPAA requirements and experience in developing systems that manage PHI in compliance with applicable regulations.

• Knowledge of SOC 2 compliance frameworks and the technical controls necessary to support certification.

• Experience with LLMs, NLP, or generative AI frameworks such as LangChain, OpenAI API, Hugging Face, or similar.

• Strong comprehension of data security, encryption, access control, and audit logging best practices.

• Excellent written and verbal communication skills, including the ability to document technical work succinctly.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Comprehensive health, dental, and vision insurance.

• Opportunities for professional development and continuous learning.

• Flexible work hours and the option for remote work.

• Collaborative and inclusive work environment.