SR Systems Engineer - Medical Devices

The Senior Systems Engineer sits within the New Product Development (NPD) function and operates as a strategic technical partner across the full product lifecycle. This is not a narrowly scoped component-level role; it demands the ability to hold system-level thinking alongside granular engineering detail translating ambiguous customer and stakeholder needs into structured, traceable requirements, and then shepherding those requirements through design, integration, verification, and validation. At its core, this position bridges component-level expertise with system-level thinking: understanding how individual parts behave under manufacturing stress, through final assembly, and ultimately within the human body during clinical deployment. Engineers in this role are expected to use simulation, production intelligence, and structured risk methods not as compliance checkboxes, but as genuine tools for accelerating innovation and improving manufacturing yield.

RESPONSIBILITIES

• Requirements Management : Development and management of Functional, Non-functional, and Interface Requirements. Creation of Customer Requirement Specifications at System, Subsystem, and Component levels.
• Body of Knowledge (BoK) Development: Ability to define, structure, and draft a Systems Engineering Body of Knowledge capturing domain standards, methods, tools, and best practices — to serve as a living reference framework for the engineering organization
• Risk Management & Quality: Lead PFMEA and DFMEA activities; maintain structured risk registers throughout the program lifecycle. Proactively identify system-level risks and drive mitigation strategies. Apply historical production data and manufacturing learning to early-stage design decisions.
• System Architecture: The engineer will define and own system architecture for medical device platforms, establishing functional decomposition, interface definitions, and design trade-off strategies that ensure scalability, reuse, and manufacturability across product families.
• Production Intelligence Analysis: Mine ERP data to identify yield patterns, change order trends, and process gaps and feed that information back to Engineering.
• Legacy Learning Integration: Systematically analyze previously released products to extract process improvements that prevent recurring issues in new launches
• Systems Level Thinking: Understand and articulate how components behave from manufacturing stress through entire final assembly, deployment in human body, expansion in different therapy cavity - including torque, pressure, voltage, and insulation property interactions across the full system. Develop deep understanding of deliverable differences across sites and the technical reasoning behind process variations.
• Cross-Assembly Knowledge: Develop cross-product-family expertise across catheters, ground wires, and machined components — identifying system-level reuse opportunities and integration insights across manufacturing sites.
• Technical mentoring: Coach and mentor engineers in structured systems thinking, validation strategy, and data-driven decision making — supporting capability development aligned to the Systems Engineering Maturity Roadmap
• Customer Centric: Participates actively in customer-facing technical discussions, contributing system-level insights that demonstrate product value and support requirements clarification.

Skill Area

Specific Competencies

Simulation and Modeling

FEA, multi-physics simulation, CAD-based analysis

Production Data Analysis

ERP data mining, yield analytics, process gap identification

Requirements Engineering

Functional, Non-Functional, and Interface Requirements authoring; Requirements decomposition at System, Subsystem, and Component levels, RTM ownership

Risk and Quality Methods

DFMEA, PFMEA, Fault Tree Analysis, CAPA

Regulatory Frameworks

ISO 13485, FDA 21 CFR Part 820, ISO/IEC/IEEE 15288

Product Development Process

Lean PD gate processes (G0–G5). Alignment of gate milestones with FMEA, simulation, and requirements deliverables

Systems Engineering Frameworks

INCOSE best practices, SEBoK principles

Digital & AI Tools

1. AI-enabled FMEA tools, simulation platforms, ERP data analytics, digital twin awareness

EDUCATION & CRITICAL EXPERIENCE

• Bachelor’s or Master’s degree in Mechanical Engineering, Biomedical Engineering ,Mechatronics or related engineering discipline.
• 8–12 years of progressive experience in systems engineering, preferably within medical devices or other regulated industries such as aerospace or automotive
• Proven track record of influencing cross-functional teams and driving measurable engineering improvements using production data and risk analysis
• Well versed with AI tools.
• Nice to Have - INCOSE Associate or Certified Systems Engineering Professional (ASEP/CSEP) certification or any Requirements Engineer certification.
• Nice to Have - AI-enabled FMEA tools, simulation platforms, ERP data analytics, digital twin awareness

LEADERSHIP AND INTERPERSONAL SKILLS

• Proven ability to influence cross-functional and globally distributed teams without direct authority — using technical credibility and data rather than organizational hierarchy
• Strong technical communication skills: capable of presenting complex system behavior and data-driven recommendations to both engineering teams and executive stakeholders
• Customer-facing experience: ability to translate customer application needs into system specifications and participate in technical validation discussions
• Proactive, system-level problem-solving orientation