דרושים » רכש ולוגיסטיקה » Junior AI Solutions Engineer

We are seeking a Senior Software Architect to lead and shape the architectural vision of our SaaS healthcare platform, guide the adoption of cutting-edge AI technologies, and ensure our solutions remain scalable, secure, and future-ready. This is a hands-on, strategic leadership role that combines visionary system design, proof-of-concept development, and AI-first innovation enablement.

Responsibilities
1. Architectural Leadership

Define, communicate, and evolve the platforms architectural vision to ensure alignment with business goals.
Lead high-level design discussions and decisions for new and existing products.
Establish and maintain architectural principles, standards, and patterns across all engineering teams to ensure consistency and coherence.
Drive cloud-native architecture in AWS with a strong focus on scalability, availability, performance, and cost efficiency.
2. Innovation & Emerging Technology

Build POCs and pilots for new solutions, frameworks, and AI capabilities.
Evaluate emerging trends in AI, LLMs, MCPs, agents, and developer productivity tools, ensuring strategic adoption where relevant.
Initiate projects/features to solve widespread problems (including work with Support, DevOps, Infosec)
3. AI Tooling Evangelism

Spearhead engineering-wide adoption of AI-assisted development workflows, including developer productivity tools, code copilots, automated quality checks, and automated documentation.
Define best practices, guidelines, and governance for AI usage in development (e.g., Cursor configuration rules, prompt engineering standards).
Educate and mentor teams on effective AI utilization for engineering, product, and operational tasks, taking into account security, costs, and compliance.
4. Technical Governance & Best Practices

Overall ownership of the system scalability, COGS, and stability of the product(s)
Define standards for coding, testing, CI/CD, and monitoring.
Enforce architectural and design reviews for key initiatives.
5. Cross-functional Collaboration

Work closely with Engineering, Product, Data Science, DevOps, and Security teams to deliver well-architected solutions.
Communicate complex technical concepts to non-technical audiences, including executives and external partners.

As a Software Engineer Intern in the MSC IL AI Innovation team, you will contribute to the design, development, testing, and delivery of AI-powered features and services for our Specialized Clouds organization. You will work with mentors and engineering team members to build reliable, secure, and responsible software, while learning modern AI engineering practices across prompts, retrieval, agents, evaluation, observability, and cloud-native development.

This internship is designed for entrepreneurial, creative, and highly motivated students who are passionate about building AI-native products, taking ownership in ambiguous 0 to 1 environments, and using AI throughout the engineering lifecycle to solve complex, high-impact problems in sovereign and regulated cloud environments.


Responsibilities
Develop, test, debug, and maintain software components for AI-powered products, services, or internal tools.

Contribute production-quality code that is maintainable, secure, performant, and aligned with team standards.

Partner with mentors, engineers, product managers, and stakeholders to understand user requirements and translate them into engineering tasks.

Contribute to AI features such as agents, tool/API integrations, prompt flows, retrieval workflows, evaluation harnesses, and monitoring capabilities.

Help define and run tests, evaluations, and quality checks for AI behavior, including accuracy, relevance, groundedness, and reliability.

Investigate issues using logs, traces, test results, and debugging tools, and incorporate feedback into improved solutions.

Learn and apply engineering best practices across code quality, security, responsible AI, CI/CD, documentation, and observability.

Document work clearly and share implementation details with team members to support continuity and knowledge sharing.

Collaborate effectively with team members in a dynamic, ambiguous environment; adapt quickly across technologies, stacks, and engineering tasks; proactively seek and incorporate feedback; and contribute to shared goals while building technical depth.

MA?LYS is a fast-growing global body care brand, built on a proprietary e-commerce platform, deep consumer insights, and a relentless focus on performance. Millions of women worldwide use our products, and we are on a mission to become one of the most influential body care brands globally.
We operate at extreme velocity, with high standards and an obsession with results. We believe in challenging conventional thinking, making data-driven decisions, and moving quickly from idea to execution.
We are looking for a Head of AI to build and lead MA?LYS' AI transformation journey, serving as a rare opportunity for a highly strategic and deeply hands-on builder who thrives at the intersection of business, technology, and execution. In this role, you will have the ultimate playground to shape how our entire company operates, influence every major function, and create a lasting competitive advantage for a leading body care brand. Initially operating as an individual contributor partnering closely with the CEO and the Management Team. Over time, you will transition to build and lead a small, high-performing team as AI becomes an increasingly critical capability across our global e-commerce and retail operations.
To thrive in our high-pace environment, you must think like an owner, not a consultant, and be equally comfortable discussing macro business strategy with executives as well as writing code to solve a messy problem yourself. You are someone who embraces ambiguity, enjoys building from scratch, and is completely obsessed with execution. You possess the high emotional intelligence and empathy required to collaborate with non-technical stakeholders and build trust, yet you maintain the thick skin, resilience, and great sense of humor needed to handle direct communication and rapid structural pivots. Most importantly, you maintain an uncompromising commitment to quality and process improvement, ensuring that every automation or workflow you deploy actively drives our core business metrics forward without requiring hand-holding or constant reminders.
Your life at MA?LYS will look like…
* Define and lead the company's AI strategy and execution roadmap.
* Identify the highest-impact opportunities for AI across all business functions.
* Prioritize initiatives based on measurable business value, speed of implementation, and scalability.
* Design, build, deploy, and continuously improve AI-powered workflows, tools, automations, and agents.
* Translate business challenges into practical AI solutions that improve performance, efficiency, and decision-making.
* Develop production-ready applications and workflows using modern AI technologies, APIs, agents, and automation frameworks.
* Drive adoption and behavioral change across teams.
* Partner directly with business leaders to identify opportunities and implement solutions.
* Evaluate emerging technologies, models, vendors, and platforms.
* Establish governance, standards, architecture, and best practices for AI development.
* Define and track success metrics for every AI initiative, ensuring measurable impact on growth, productivity, speed, efficiency, or cost.
You will thrive in this role if you have…
* Strong technical foundation in Computer Science, Software Engineering, Data Science, or a related field.
* 5+ years of experience building technology, data, AI, automation, or software solutions in high-performance environments.
* Proven experience identifying business opportunities and translating them into deployed AI products, agents, automations, or workflows that drive measurable business impact.
* Hands-on experience working with modern generative AI technologies, LLMs, agent frameworks, APIs, embeddings, automation platforms, and production deployments.
* Strong proficiency in Python, SQL, APIs, and data-driven application development.
* Deep understanding of business operations, growth metrics, p

We are looking for a detail-oriented and technically skilled Senior QA Engineer to join our engineering team and help ensure the quality, reliability, and security of our product.
In this role, you will own the quality of complex cybersecurity features from design through production, working closely with developers, product managers, and automation engineers. You will play a key role in defining test strategies, identifying risks early, and ensuring high-quality releases.
As part of your daily workflow, you will leverage AI-powered tools to improve test planning, discover edge cases, investigate defects, analyze logs, and enhance overall testing efficiency.
The Responsibilities:
Own the automation domain end-to-end: strategy, architecture, implementation, and continuous improvement
Design, build, and maintain scalable test automation frameworks across UI, API, integration, and system layers
Develop hands-on automation code in Python and/or TypeScript
Drive quality standards across engineering teams, partnering closely with developers, QA, DevOps, and product stakeholders
Define and implement automation best practices for reliability, scalability, maintainability, and fast feedback
Integrate and optimize automation within CI/CD pipelines to enable rapid, confident releases
Test complex SaaS, cloud, and cyber-related product flows, including security-sensitive scenarios, permissions, integrations, and system behavior
Use AI tools to support automation development, test generation, debugging, log analysis, documentation, and coverage improvement
Proactively identify coverage gaps and improve system testability, observability, and debuggability
Participate in system design and feature planning to embed quality from the earliest stages
Build and evolve testing environments using scripting, infrastructure-as-code, and cloud-native tools
Mentor and guide engineers, elevating automation standards across the organization

Were looking for a Data Engineer to join our company. This is a hands-on, cross-functional role focused on owning and improving our data quality, pipeline reliability, and modeling infrastructure. Youll be responsible for managing and optimizing our DBT models, ETL processes, and data connectors - and will work closely with product, operations, and engineering teams to ensure data is accurate, consistent, and accessible.

You should be someone who thrives on autonomy, takes pride in clean and scalable solutions, and enjoys helping others get the data they need.

In this role you will
Architect our entire DBT project and data warehouse structure completely from scratch-a rare, high-impact opportunity
Lead data modeling efforts: Translating business needs into scalable, reliable data models
Build and monitor data pipelines and connectors from various internal and external systems
Ensure high standards of data quality, consistency, and documentation
Collaborate with engineering, product, and operations teams to understand needs and build data solutions
Proactively detect data issues and work to resolve them
Help define and improve internal data best practices and standards
Support data governance efforts and advocate for trustworthy, well-modeled data
You will be one of the pioneers of a new team - passion & energy is required

You will manage the AI Platform Engineer(s), set the technical standards for the AI Power User group's citizen development program, and serve as the connective tissue between business leadership, platform owners, and development teams. You will shape the multi-year AI architecture roadmap while also rolling up your sleeves to conduct architecture reviews, resolve blockers, and move use cases from concept to production. This is a role for someone who can think big and execute - and who understands that in an enterprise context, the quality of your governance is inseparable from the quality of your architecture.

What You'll Own
Strategy & Architecture
Define and own the enterprise AI integration strategy - identifying opportunities to embed intelligent automation, agentic workflows, predictive analytics, and generative AI capabilities across our company core platforms
Develop and maintain reference architectures, design patterns, and the AI architecture decision log that governs how AI models connect to enterprise systems and what they are permitted to do
Consult on enterprise system architecture and implement best practices for the Enterprise Business Systems team to leverage in their day-to-day execution.
Lead Proof-of-Concept initiatives for new AI tools and platform-native AI features, evaluating them against build-vs-buy criteria before recommending adoption
Partner with business stakeholders to translate operational pain points into AI use cases with clear ROI framing and sequencing criteria
Contribute to our enterprise data strategy, ensuring AI initiatives are supported by clean, accessible, and well-governed data pipelines
Integration Architecture & Delivery

Design and own the Workato eMCP layer - the MCP governance model, persona-scoped token framework, workspace isolation strategy, and the single sanctioned action surface through which all AI agents write back to enterprise systems
Define integration patterns and standards for AI model connectivity (Claude, ChatGPT) to Salesforce, NetSuite, HiBob, and Jira - specifying what agents can read, what they can write, through which surfaces, and with what confirmation and audit requirements
Design and oversee API strategies, event-driven architectures, and middleware patterns that support scalable AI feature delivery - including agentic workflows, intelligent data transformation, anomaly detection, and natural language interfaces layered onto ERP and CRM data
Collaborate with Engineering during build phases, conducting architecture reviews, providing hands-on guidance, and resolving complex technical blockers
Define non-functional requirements - latency, security, auditability, model drift monitoring - for AI components embedded in mission-critical business processes
Establish MLOps and LLMOps practices appropriate for our enterprise environment: model versioning, observability, and rollback procedures for production AI workloads

1. AI Architecture & Technical Leadership

Guide AI architectural direction across platform, focusing on system design, model lifecycle, and integration of AI components into product workflows.
Act as a senior technical reviewer and thought partner for complex or cross-team AI design decisions.
Provide technical oversight on areas such as exploring new models/technologies/opportunities for the company
Surface architectural risks, tradeoffs, and long-term implications, including cost, security and compliance aspects and clearly advise the VP of AI when certain technical directions should not be pursued.
This role influences judgment, clarity, and experience, not through blocking authority.

2. Hands-on Applied Innovation (Core Pillar | ~50%)

Spend at least 50% of time hands-on, building:
End-to-end AI prototypes
Technical demos and proofs of concept
Exploratory implementations of new AI capabilities
Drive applied innovation that:

De-risks new technologies
Demonstrates feasibility and impact
Informs product direction and business opportunities
Build fast, concrete examples that teams can learn from and extend.
Transition successful prototypes to team ownership for further development and scaling.
This role is expected to lead AI innovation by doing, while working closely with product, medical and engineering

3. Best Practices & Technical Enablement

Define and promote best practices for applied AI development, including:
Rapid prototyping and vibe coding.
Agent design, orchestration, and evaluation patterns
Experimentation, benchmarking, and validation workflows
Help teams align on shared technical patterns, tools, and standards.
Identify opportunities to consolidate duplicated efforts and improve cross-team coherence.
Lead technical deep dives, architecture discussions, and design reviews.
4. AI Compliance & Regulatory Enablement (Technical Scope)

Ensure Navinas AI development practices align with applicable AI regulations for a software product handling sensitive medical data.
Define and guide AI-specific compliance practices, including data usage, transparency, evaluation, and documentation expectations.
Support and contribute to AI-related compliance and regulatory documentation, in close collaboration with Legal, Security, and Medical Research teams.
Serve as a technical point of reference for AI compliance questions.

Were looking for a highly motivated AI Developer to help design, build, and deploy intelligent agentic systems across our product ecosystem. In this role, you'll work at the intersection of machine learning, backend systems, and modern frontend technologies to deliver AI-first features that feel magical to users.
This is a hands-on, cross-functional role ideal for engineers who love building full-fledged features-from data pipelines and LLM orchestration to intuitive UI experiences-with a strong product mindset.
Responsibilities:
AI Agent Design & Integration:
Design and implement autonomous or semi-autonomous agents using LLMs (e.g., OpenAI, Anthropic, open-source models).
Work with prompt engineering, RAG pipelines, and tool/plugin integrations to enable agents to interact with internal and external systems.
Build scalable agent runtimes and orchestration layers (e.g., LangChain, Semantic Kernel, ReAct-based agents).
Fullstack Product Development:
Own full-stack features end-to-end: from backend APIs and data models to React-based frontend interfaces.
Integrate AI/agent capabilities into customer-facing products with clean UX and measurable performance.
Collaborate closely with design, product, and data teams to bring ideas from concept to production.
Systems & Infrastructure:
Build and maintain backend services and pipelines that support AI agents, including vector search, embeddings, function calling, and observability.
Optimize inference flows for performance and cost, potentially using streaming, caching, or local model inference.
Ensure systems are secure, reliable, and compliant with InfoSec standards.
Experimentation & Continuous Improvement:
Rapidly prototype and iterate on new AI capabilities and user experiences.
Analyze performance and usage metrics to drive product and model improvements.
Stay up to date with the evolving AI toolchain and emerging agent architectures.