AI/ML Engineer Jobs in Florida, NY at Technovera

Position: AI/ML Engineer
Type: Full-time | Remote | Technovera
Contact: priya

About the Role

As one of the first AI/ML hires, you will have the unique opportunity to:

• Work on groundbreaking projects combining large language models (LLMs) and traditional machine learning.

• Develop novel research synthesis techniques using NLP and demonstrate their business impact.

• Collaborate closely with external partners and clients to drive product innovation and growth.

• Contribute to product decisions, direction, and prioritization.

• Help shape the engineering culture and set best practices for AI development.

The platform is already in production with 20+ enterprise clients and tens of thousands of calls, offering rich datasets to improve and build upon.

Example Projects You Might Work On

• Search ranking algorithms integrating user interaction signals (edits, copies, engagement) with semantic and structured search.

• An in-house prompt optimization system that uses production signals or human labels to refine prompt quality and model output.

• Developing and open-sourcing a benchmark suite to evaluate LLM outputs on domain-specific tasks.

• Designing an entity deduplication and resolution system leveraging internal signals and external data.

• Building a knowledge graph pipeline that transforms unstructured interview data into dynamic, evolving knowledge representations.

About You

The ideal candidate is a product-minded ML Engineer who:

• Has strong knowledge in building LLM-powered systems from the ground up in a product-focused environment.

• Is comfortable working across the stack: data gathering, logging, experimentation, prompt engineering, and deployment.

• Communicates clearly and can translate complex technical ideas for non-technical stakeholders.

• Has a strong bias toward action and can deliver large projects end-to-end while collaborating with senior stakeholders.

• Is excited about building and mentoring an AI/ML team over time.

Preferred Knowledge

• NLP techniques (text classification, entity resolution, retrieval-augmented generation).

• Prompt optimization and evaluation pipelines.

• Knowledge graph development.

• Search and ranking infrastructure.