AI Engineering Consultant

Job Description

Job Description

Project Overview

HireScale is partnering with an innovative organization looking to accelerate their AI capabilities and embed intelligent systems into core workflows. This engagement is built around rapid 0→1 product development —taking ideas from concept to working prototypes and MVPs with speed, precision, and real-world applicability.

The focus is on shipping, not speculating . You’ll leverage AI-assisted development as a force multiplier—using modern tooling to compress build cycles, iterate quickly, and validate ideas in-market—without compromising on code quality, system design, or long-term scalability.

This is a hands-on role for a consultant who can operate across the full lifecycle:

• Rapidly translating ambiguous ideas into tangible prototypes
• Building MVPs that balance speed with solid engineering fundamentals
• Making smart trade-offs between velocity and technical rigor
• Establishing patterns that can evolve from prototype → production

You’ll work at the intersection of product, engineering, and AI—bringing structure to fast-moving environments while maintaining a high bar for accuracy, performance, and maintainability.

The end goal isn’t just to build something quickly—it’s to build the right thing, prove it works, and set the foundation to scale it.

Scope of Work & Core Capabilities

• Design and ship AI-driven solutions aligned to clear business outcomes
• Translate ambiguous use cases into fast, working prototypes and MVPs
• Build, test, and deploy scalable AI applications end-to-end
• Apply strong engineering fundamentals to balance speed, accuracy, and maintainability
• Optimize models, pipelines, and system performance for production readiness
• Leverage modern AI/ML frameworks and LLMs (OpenAI, Anthropic, LLaMA, Mistral)
• Develop across Python, JavaScript/TypeScript, and SQL-based environments
• Utilize frameworks like LangChain, LlamaIndex, PyTorch, TensorFlow, FastAPI, and Node.js
• Deploy and manage infrastructure across AWS, GCP, or Azure using Docker and modern hosting platforms (Vercel, Netlify)
• Work with vector databases and data systems (PostgreSQL, Pinecone, Weaviate, FAISS)
• Collaborate with stakeholders and communicate complex concepts clearly
• Document solutions and enable clean handoff for internal teams

Engagement Brief

This consulting engagement is focused on delivering real, production-ready AI outcomes within a defined timeframe by balancing speed, scalability, and measurable business impact. It’s structured for experienced builders who can own execution end-to-end and deliver work that lasts beyond the engagement.

• Target Outcomes: Production-ready AI solutions aligned to defined use cases, delivering measurable improvements in efficiency, accuracy, or cost, with scalable architecture and a clean handoff for internal teams
• Duration: 8–12 weeks, with potential extension based on outcomes
• Commitment: Full-service or fractional, depending on scope
• Budget: Flexible based on timeline, scope, and proposal
• Start Date: ASAP

Portfolio Requirements:

• 2–3 relevant AI/ML projects (production or near-production)
• Demonstrated end-to-end ownership (problem → solution → outcome)
• Clear evidence of impact (metrics, business results, or adoption)
• Supporting materials such as GitHub, case studies, or technical write-ups preferred

About HireScale

HireScale is redefining how people and organizations grow and work together. We connect companies with top-rated independent consultants to build agile, highly skilled and resilient teams; while also empowering talent to shape their own paths and align meaningful work with personal freedom and purpose. Our goal is a future where opportunity is open, work is self-directed, and everyone thrives.