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Required Program QA Engineer
Department: QA
Reporting to: Director of Quality
Responsibilities:
Review of Contract ,Customer SOW and SPEC requirements to ensure compliance of design and production.
Approve gates review at R&D
PLCM (Project Life Cycle Management) process control, full partnership in project management during the development, testing, production, customer delivery and customer support phases throughout the product life
Identifying regulatory Gaps in new and carrent projects
Perform Internal and External Audits
QA presence in R&D process (capacity) and ability to prevent errors in design
Follow up after R&D corrective and preventive actions and Improvement Plans
Transfer products from development to NPI and serial production (QA activities)
Review and approval ECO / MCO / ATP/ATR, Waiver relate to R&D , ENG
Writing QA procedures
FRACAS Management
Quality Assurance Program Plan (QAPP) and the Quality Allocation Tasks for the program.
Customer complince relate to NPI , Pre production , design errors
Perform Quarterly data analysis for QBR.

We are seeking a hands-on Tech Lead to be the first technical hire for our newly formed ML team. This is a unique ground-floor opportunity to build something from scratch and have direct influence on the technical direction of our ML capabilities. This role combines deep technical expertise with foundational leadership, requiring someone who can architect solutions, establish technical standards, and lay the groundwork for future team growth. As our founding ML tech lead, you'll have the opportunity to shape our technology choices, define our technical practices, and build the fundamental skill set that will guide the team as we scale.
Hybrid
Full-time
What youll do: 
Shape the technical foundation: As our first ML hands on hire, choose and establish the team's core technology stack, frameworks, and development practices together with the ML leader
Build from the ground up: Design and implement our initial ML infrastructure, data pipelines, and model deployment frameworks
Define technical standards: Establish coding standards, best practices, and processes that will guide future team members
Hands-on development: Lead by example through direct development and deployment of ML models and data science solutions
Partner with stakeholders: Work closely with business teams to identify initial ML opportunities and build foundational proof-of-concept models
Establish MLOps practices: Set up our model development, testing, deployment, and monitoring workflows from scratch
Recruit and onboard: As the team grows, play a key role in technical interviews and help onboard new ML engineers and data scientists
Create team culture: Help establish the team's technical culture, learning practices, and collaboration methods
Technology evaluation: Research and select the tools, platforms, and technologies that will form our ML tech stack
Document and share knowledge: Create foundational documentation and knowledge-sharing practices for the growing team.