Job Overview

This is a hands-on technical role responsible for designing, building, and deploying AI solutions directly from business requirements across strategy, product management, business development, sales, and marketing.
This role focuses on applied, outcome-driven AI — rapid experimentation, practical deployment, and measurable business impact. The engineer works closely with non-technical stakeholders to translate ambiguous problems into usable AI-powered tools and workflows, iterating quickly based on real-world feedback.

Roles and Responsibilities:

Solution Design & Development

•    Design and build applied AI solutions (ML and GenAI) based on business requirements
•    Develop end-to-end AI workflows, including data ingestion, modeling, prompt engineering, orchestration, and output delivery
•    Rapidly prototype proofs-of-value and evolve them into deployable solutions
•    Integrate AI capabilities into existing enterprise tools and workflows (e.g., CRM, analytics platforms, internal applications)

Business Partnership

•    Work directly with Strategy, Product, Sales, Marketing, and BD teams to clarify problems and define AI-enabled solutions
•    Translate loosely defined business needs into technical approaches and working solutions
•    Act as a technical thought partner, advising what is feasible, scalable, and worth pursuing

Experimentation & Iteration

•    Operate in a fast-paced, experimental environment with short build-test-learn cycles
•    Iterate solutions based on user feedback, adoption data, and business outcomes
•    Balance speed and pragmatism with appropriate technical rigor and governance

Deployment & Enablement

•    Support deployment of AI solutions into production environments (with IT/data partners as needed)
•    Document solutions and enable business users to effectively adopt and use AI tools
•    Contribute to reusable components, patterns, and best practices for applied AI

Qualifications:

Technical Skills

•    Strong proficiency in Python
•    Hands-on experience with machine learning
•    Experience working with LLMs, prompt engineering, RAG architectures, or AI agents
•    Familiarity with ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
•    Experience integrating APIs and working with data pipelines
•    Ability to prototype quickly using modern AI tooling and cloud-based services

Business & Collaboration Skills

•    Ability to work directly with non-technical stakeholders
•    Strong problem-framing and requirement translation skills
•    Clear communication of technical concepts to business audiences
•    Comfort operating with incomplete information and evolving requirements

Preferred Qualifications

•    Experience building AI solutions for commercial, marketing, operations, or product use cases
•    Exposure to enterprise data environments and systems
•    Experience deploying AI solutions beyond proof-of-concept
•    Familiarity with AI governance, data privacy, and responsible AI practices

Success Measures

•    Speed from idea to working solution
•    Adoption and sustained use of AI solutions by business teams
•    Demonstrated business impact (e.g., revenue enablement, efficiency gains, decision quality)
•    Ability to independently deliver applied AI solutions