Softvery Solutions

About the Client

Israeli healthcare-tech startup working on minimally invasive endovascular decision support. Our client integrates with hospital IT environments and operate under strict security and compliance standards (HIPAA/GDPR/MDR).

Role Overview

This is an IT operations role (≈80% IT, ≈20% light DevOps). Your mission is to keep our people, devices, networks, and core tools running smoothly and securely, while supporting engineers with basic cloud/CI tasks. You won’t be building product infrastructure from scratch; you’ll support internal processes and day-to-day operations.

What You’ll Do

IT Operations (Primary)

• Own endpoint & identity lifecycle: device provisioning (Win/Mac), MDM/Intune/Jamf, SSO/IAM, MFA, least-privilege access.
• Manage Microsoft 365 tenant (Exchange, SharePoint, OneDrive, Teams), Atlassian (Jira/Confluence), and other corporate SaaS
• Administer corporate network: Wi-Fi/VPN, basic firewall rules, secure remote access to cloud resources.
• Operate IT security hygiene: patching, EDR/AV, disk encryption, vulnerability scans, email security, backup of critical IT data.
• Run joiner/mover/leaver flows: accounts, permissions, laptops, tokens, licenses.
• Provide Tier-2 support, escalate to vendors where needed, maintain clear SLAs and ticket hygiene.
• Keep documentation current: IT runbooks, access matrices, asset inventory, incident & change logs.
• Support audit readiness with evidence collection for ISO 27001/SOC 2/HIPAA as guided by compliance.
  
   

Light DevOps (Secondary / Basic)

• Maintain existing CI/CD workflows (GitHub Actions/Bitbucket Pipelines) created by engineering; fix small issues, update secrets.
• Perform basic AWS ops (IAM users/roles, S3 buckets, EC2 start/stop, CloudWatch alarms), following playbooks.
• Manage artifacts and package repos (e.g., Nexus/Artifact Registry).
• Assist with monitoring/alert routing, log retention policies, and cost/usage tagging.
• Write small automations/scripts (Bash/Python/PowerShell) to reduce manual IT tasks.
  
   

Minimal Requirements

• 3+ years in IT Systems Administration (corporate IT, M365/Google Workspace, identity, networking, security basics).
• Hands-on with Microsoft 365 admin, endpoint management (Intune/Jamf or similar), SSO/MFA, and VPN.
• Working knowledge of AWS basics (IAM, S3, EC2, CloudWatch) and GitHub Actions/Bitbucket enough to maintain existing pipelines.
• Solid grasp of IT security hygiene: patch management, EDR/AV, encryption, role-based access, logging.
• Experience supporting collaboration stacks (M365, Jira/Confluence, Slack/Teams) and managing licenses/assets.
• Scripting for automation (PowerShell and/or Bash/Python).
• Strong documentation habits and English communication skills (written & spoken).
  
   

Nice to Have

• Exposure to healthcare or other regulated environments (HIPAA/GDPR/ISO 27001/SOC 2).
• Basic IaC familiarity (Terraform) to apply small changes from templates.
• Experience with Nexus/Artifactory, Docker fundamentals, and cloud cost monitoring
• Participation in audits, evidence gathering, or security incident response.
  
   

How You Work

• Service-oriented, organized, and calm under pressure.
• Security-minded with a practical, usability-balanced approach.
• Proactive about closing gaps, improving runbooks, and automating repetitive tasks.
• Comfortable in a startup: hands-on, flexible, able to switch context between helpdesk tickets and admin tasks.
  
   

What This Role Is Not

• Not a senior DevOps/Platform role building Kubernetes clusters or greenfield cloud architectures.
• Not responsible for designing product infrastructure from scratch.
  
  
   

Tools You’ll Touch

Microsoft 365, Intune/Jamf, Azure AD/Entra ID, AWS (IAM/S3/EC2/CloudWatch), GitHub/Bitbucket, Jira/Confluence, EDR/AV, VPN/firewall, Nexus.

We already have an ML Engineer on the project (Python, classical ML, RAG, LangChain, vector DBs, AWS.) and an MVP AI solution in place.
 

We’re looking for a short-term consultant to review, stress-test, and improve this solution.Objective of the Engagement
 

Your main goal will be to audit the current AI/ML implementation (support chatbot + recommendation engine) and provide clear, actionable recommendations to make it:
 

• More accurate and trustworthy for users
• Provide solutions for the speed optimization
• Technically robust and maintainable for the team

• Cost-efficient and scalable as usage grows

   

Key Responsibilities
1. Architecture & Code Review
 

• Review current AI/ML architecture: data flows, pipeline, model choices, and integrations with mobile backend and admin panel.
• Review codebase (Python, ML libraries, LangChain/RAG pipelines, vector DB usage, prompt design, evaluation scripts).

• Assess MLOps practices: environments, versioning, deployment, monitoring, rollback strategy.

   

2. AI Support Chat & RAG Analysis
 

• Evaluate the virtual assistant:
  • Retrieval quality (documents, embeddings, chunking strategy).
  • Prompting strategy and guardrails (hallucination risk, tone, safety).
  • Latency, cost, and failure handling for LLM calls.
• Propose improvements to:
  • Retrieval and ranking
  • Prompt templates / system messages

  • Evaluation methods (e.g., answer quality, hallucination rate).

     

3. Cost, Performance & Scalability
 

• Review current infrastructure and model choices for response time and cost (LLM provider, embeddings, vector DB).

• Suggest optimizations: caching strategy, batch calls, model selection (cheaper/faster models for certain flows), index design, etc.

   

4. Collaboration & Knowledge Transfer
 

• Work closely with the existing ML Engineer to:
  • Validate proposed changes
  • Prioritize a realistic improvement roadmap
  • Share best practices in LLM/RAG & recommender systems.

• Deliver clear documentation and a short handover so the team can continue without you.

   

Required Profile
Must-have
 

• Solid experience as a ML Engineer / ML Architect / LLM Engineer working on production systems.
• Strong hands-on skills with Python and common ML / data stack (Pandas, scikit-learn, PyTorch or similar; SQL).
• Proven experience designing and shipping LLM-based applications, specifically:
  • RAG pipelines (vector DBs, embeddings, retrieval, ranking).
  • Prompt engineering and evaluation.
  • Integrating LLMs into real products (APIs, mobile/web backends).
• Experience with recommendation systems (content-based, rules/hybrid, ranking) in real-world products.
• Good understanding of MLOps: versioning, CI/CD for models, monitoring, logging, alerting.
• Ability to perform an independent audit: challenge existing choices respectfully, explain trade-offs to non-ML stakeholders.

• Comfortable writing clear technical documentation and presenting findings.

   

Nice-to-have
 

• Background in consumer apps, travel, or edtech (recommendation, search, personalization).
• Awareness of data privacy and compliance (LGPD, GDPR or similar) in ML/AI solutions.
• Type: Freelance / consulting