דרושים » AI » AI Engineer - Foundational and VLMs

Were looking for a AI Solutions Junior Engineer who thrives at the intersection of data, product, and client impact. In this role, youll take full ownership of delivering and evolving AI based data solutions for our customers - blending deep analytical skills with strong communication and product intuition. If youre excited by semantic models, data analysis, and helping clients leverage data to inform business decisions, this role is for you.

What Youll Do
Work with AI-generated semantic models, editing them and optimizing them to drive actionable insights.
Work directly with customer stakeholders to deeply understand business needs, use cases, and their data environment.
Own the technical solution end-to-end, including model refinement, tuning and verification, ensuring data accuracy and consistency.
Collaborate with R&D, Sales and Product to surface insights, resolve blockers, and influence product enhancements.
Translate business questions into analytical solutions constantly pursuing optimization, efficiency and automation opportunities.
Work with client stakeholders to provide training and enablement support.

We are looking for a technically-grounded, visionary Product Owner to lead GenAI-powered initiatives within our B2B platform. You will stand at the intersection of cutting-edge LLM technology and enterprise-scale business impact, transforming how global financial institutions handle everything from underwriting automation to complex claims intelligence.

Key Responsibilities:
AI Roadmap & Strategy: Define and own the GenAI roadmap, identifying high-value use cases like fraud detection copilots, document intelligence, and agentic underwriting.
Backlog Excellence: Own and optimize the product backlog, translating complex AI concepts into clear user stories and acceptance criteria for engineering and data science teams.
Technical Specs & Orchestration: Read and write technical specs, API contracts, and data models. Youll work closely with Development & architecture teams
Experimental Mindset: Lead lean experiments and A/B tests to validate LLM performance, managing the balance between probabilistic AI outputs and deterministic business rules.
Stakeholder Leadership: Communicate the vision and progress transparently to global stakeholders and enterprise clients, acting as the bridge between "the possible" and "the practical".

We are looking for an experienced Backend Engineer (4+ years) to join the team as an AI Integration Engineer, supporting one of our enterprise clients as part of their AI-Native Development program.

In this role, you will act as an AI Champion and Technical Mentor within the clients R&D organization. Your mission is to help development teams effectively integrate AI tools, agentic coding workflows, and best practices into their daily software development lifecycle (SDLC). You will help guide teams through AI adoption, help overcome resistance, and ensure AI delivers real value.

This is not a feature-delivery or coding-focused role, especially in the early stages, the position is centered on enablement, training, and integration, working closely with teams to embed AI development tools into real workflows and support the organizations AI transformation.

This is a high-impact role focused on technical enablement, AI expertise, and effective collaboration with development teams and intended for experienced engineers who actively use AI in their day-to-day development work and are looking to influence how teams work. Success in this role looks like teams shipping faster and smarter with AI, not lines of code written.



What Youll Do:

● Help drive adoption and optimization of GitHub Copilot and AI dev tools

● Support (mostly) backend engineers, answer technical questions and help unblock teams

● Continuously track the AI tooling landscape and best practices

● Measure impact, identify gaps and improvement opportunities across the SDLC

● Navigate and reduce change resistance to change with patience and empathy

● Partner with DevOps, architects, and management to track and optimize AI utilization

● Build and maintain a library of reusable AI assets (prompts, rules, agent workflows, playbooks) tailored to the clients stack

● Serve as the go-to escalation point for GitHub Copilot-related issues, edge cases, and feature rollouts