Contract Practitioner – Generative AI Integration… at Nectar · Remote

Role Overview

Alchemy is seeking a qualified Practitioner with applied, real-world experience in Generative AI Integration for Developers to participate in a skills assessment validation engagement. This is a short-term, contract, remote engagement in which the Practitioner will complete a practitioner-level skills assessment and a brief post-assessment survey. This role does not involve teaching, instructional design, content creation, or ongoing advisory responsibilities.

Engagement Details

Engagement Type: Contract / 1099 – Short-term engagement

Location: Remote

Estimated Item Count: ~90

Estimated Time to Completion: Approximately 1.5–2.5 hours

Assessment Window: Work must be completed within a defined access window.

Scope of Work

Complete a practitioner-level skills assessment used for validation and standard-setting purposes.

Complete a short post-assessment survey providing feedback on the assessment experience.

This role does not include:

• Teaching or facilitation responsibilities

• Instructional or curriculum design work

• Content authoring or SME review of materials

• Ongoing advisory or consulting responsibilities

Required Expertise

The Practitioner should be a current software engineer / developer with applied, real-world experience related to the following knowledge areas and skills:

Integrating Generative AI for Developers

• Develop a comprehensive technical implementation plan for integrating generative AI into existing systems

• Identify key technical requirements and dependencies for generative AI deployment

• Decide on architecture that supports scalable generative AI operations

• Create a phased rollout strategy to minimize disruption and manage risks

• Establish performance metrics and monitoring processes for generative AI systems

Aligning Generative AI with Business Cases

• Analyze business processes to identify opportunities for generative AI implementation

• Evaluate the potential ROI of generative AI applications across different business functions

• Develop use-case-specific strategies for integrating generative AI into product workflows

• Apply a framework for prioritizing generative AI initiatives based on business value and feasibility

• Design a pilot program to test generative AI in real-world contexts

Ensuring Interoperability in Generative AI Systems

• Assess the interoperability requirements for generative AI within an organization's technology ecosystem

• Create protocols to facilitate seamless integration with existing systems

• Develop strategies for managing version compatibility and updates across integrated AI systems

• Establish governance for maintaining interoperability as generative AI technologies evolve

Security for Generative AI Integrations

• Identify security vulnerabilities specific to generative AI

• Apply mitigations for various types of GenAI security vulnerabilities

• Develop strategies to protect against adversarial attacks and model manipulation

• Create incident response plans tailored to generative AI security breaches

Effective Cost Management for Generative AI

• Estimate the total cost of ownership for generative AI implementations

• Optimize computational resources for transfer learning models

• Evaluate the factors that impact Generative AI costs

• Apply techniques for reducing data storage and transfer costs associated with large/complex AI models

• Create budgeting and forecasting models for long-term generative AI initiatives

Scaling Integrated Generative AI

• Assess infrastructure requirements for supporting large-scale generative AI initiatives

• Design scalable architectures capable of handling increasing AI workloads and data volumes

• Implement load balancing and distributed computing strategies for integrated generative AI

• Build disaster recovery and product continuity plans specific to generative AI infrastructure

Applied Developer Workflow Integration

• Leverage AI-powered tools in day-to-day development workflows, including code generation, code completion, testing, and documentation

Ideal Candidate Profile

Active software engineer or developer with hands-on experience integrating generative AI into production systems or developer workflows.

Practical, working knowledge of how the concepts listed above are applied in real professional settings.

Does not need to be an academic researcher or industry thought leader — applied experience is what matters.

Deliverables

Completed skills assessment within the defined access window.

Completed post-assessment survey.

Compensation

This is a flat-fee engagement, paid upon successful completion of the assessment and survey.