AI / ML Consultant
Summary
Consultant designs and implements AI/ML solutions for tech companies and investment firms, assessing risks and opportunities while mentoring teams on data science and AI best practices.
What we believe
Our role
What we value
Overview
- Work with technology companies to identify improvements of existing technology, organizations, processes, and tools used to develop software products and services.
- Work with private equity companies and other investment firms to help them understand the technology strengths, risks, and opportunities for improvement for potential investments (I.e. technical due diligence)
- Design and implement solutions from scratch (e.g. Agile methodology transformations), guiding and mentoring more junior software developers and other engineers.
- Collaboratively work in partnership with internal and client technical leads and team members.
Expected Results
- Be an apprentice consultant for your first 2 projects and then be willing and able to drive a project on your own to successful completion, including collaboration, project management, and getting your hands dirty
- Participate in and drive AI assessment efforts on behalf of investment companies, collaborating, digging deep, and creating a report for the investors to outline technical investment risks
- Participate in hands-on implementation projects using processes and technologies that are most applicable to you, producing stellar customer feedback along the way
- Work with technology companies to identify improvements of existing AI technology, organizations, processes, and tools.
- Work with private equity companies and other investment firms to help them understand the technology strengths, risks, and opportunities for improvement for potential investments.
- Collaboratively work in partnership with internal and client technical leads and team members
Requirements
- In-depth expert-level experience in Data Science domain (data mining, statistics, machine learning, etc.) and AI domain:
- Analytical and generative AI
- Evaluation of available data assets and their profile
- Current knowledge of LLM landscape, commercial and open course
- Model selection, training, fine-tuning
- Model efficacy metrics, bias management
- IP considerations
- ML Ops and related tooling
- Cloud hosting options and self-hosting, and related economics
- Multi-modal AI (text, NLP / voice, images, video)
- AI-enabled software engineering
Education
- Advanced degree in computer science, statistics, advanced analytics, data science, machine learning, AI
Originally posted on Himalayas