AI Engineer - R&D to Production

Flatgigs

What You ll Work On

• Solve complex AI problems across:
• Video understanding and processing
• Text-based AI / NLP systems
• Audio processing (nice to have, not mandatory)
• Design, experiment, and iterate on AI models and approaches
• Work on problems that may not have clear starting points or predefined solutions
• Explore multiple approaches before arriving at optimal solutions
• Contribute to research-driven experimentation and innovation

What We re Looking For Core Requirement

• 4 5+ years of experience in AI / Machine Learning

This level of experience matters because it typically brings exposure to multiple AI domains, not just one.

Multi-Modal Exposure (Important)

• Experience across at least two of the following areas:
• Video
• Text (NLP / LLMs)
• Audio (bonus)

Candidates limited to only one vertical may struggle in this environment.

R&D Experience (Critical)

• Prior experience working in research or experimentation-heavy environments
• Exposure to:
• Prototyping
• Testing hypotheses
• Iterative experimentation
• Experience contributing to:
• Research initiatives
• Internal innovation projects
• Publications (nice to have, not mandatory)

Problem-Solving Depth

• Ability to go beyond making things work and instead:
• Understand why something works or fails
• Debug deeply at the model or system level
• Explore multiple approaches before finalizing a solution

Ideal Candidate Profile

• Curious by nature asks why more than what
• Comfortable working in uncertain, ambiguous problem spaces
• Enjoys experimentation and is not discouraged by failure
• Thinks in terms of systems, patterns, and root causes
• Has a strong tinkerer mindset explores beyond surface-level fixes

What Success Looks Like

• You are able to approach open-ended problems without predefined solutions
• You explore multiple directions instead of settling for the first working solution
• You contribute to building deep understanding, not just outputs
• You can work across different AI problem types without becoming a bottleneck

Nice to Have

• Research papers, publications, or contributions to open-source AI projects
• Experience with:
• Multi-modal models
• LLMs and advanced NLP
• Computer vision systems
• Audio processing systems