AI / ML Consultant

About Crosslake Technologies and Our Philosophy

Operating remotely across the United States, Crosslake Technologies champions a programmatic and forward-thinking method for managing technology through the investment lifecycle. We believe in the transformative power of technology and its growing importance in shaping business success. We are committed to empowering innovators who seek to buy, build, and operate superior technological solutions.

Our Values

• Service: Creating impact by enabling others to succeed.
• Curiosity: Providing insightful guidance through profound understanding.
• Credibility: Delivering expertise that is earned and validated.
• Commitment: Honoring the privilege to support clients in critical stages.
• Creativity: Constantly inspired to improve and innovate.

Key Responsibilities

• Collaborate with technology firms to identify opportunities for enhancing existing technologies, organizational structures, processes, and tools used in software development.
• Partner with private equity and investment entities to evaluate technology strengths, risks, and improvement opportunities within potential investments through technical due diligence.
• Design and implement solutions from the ground up, such as Agile transformations, while mentoring junior engineers and developers.
• Work closely with internal teams and client technical leads to ensure project success and innovation.

Performance Expectations

• Start as an apprentice consultant on initial projects, progressing to independently leading projects that encompass collaboration, project management, and hands-on involvement.
• Contribute to AI assessment initiatives for investment firms by conducting thorough analyses and preparing detailed reports outlining technical investment risks.
• Engage in hands-on project implementations using applicable processes and technologies, achieving excellent client feedback.
• Continue collaborating with technology companies and investment clients to identify and enhance AI technologies, organizational structures, and development processes.

Expertise and Requirements

• Extensive expert-level knowledge in Data Science including data mining, statistics, machine learning, and artificial intelligence domains.
• Competence in analytical AI and generative AI methodologies.
• Ability to assess and profile available data assets effectively.
• Up-to-date understanding of the Large Language Model (LLM) ecosystem both commercial and open source.
• Experience in model selection, training, fine-tuning, and understanding of model efficacy metrics along with bias management.
• Awareness of intellectual property considerations relevant to AI models.
• Familiarity with ML Ops practices and toolsets.
• Knowledge of cloud hosting, self-hosting options, and associated economic implications.
• Proficiency in multi-modal AI technologies encompassing text, natural language processing, voice, images, and video.
• Experience with AI-enabled software engineering techniques.

Educational Background

Possession of an advanced degree in relevant fields such as computer science, statistics, advanced analytics, data science, machine learning, or artificial intelligence is required.