Job Description
Key Responsibilities
Technology Strategy & Architecture
• Partner with the SVP of Technology to define technical strategy, architecture standards, platform direction, and modernization priorities.
• Review major architecture decisions across software, data, AI/ML, cloud, infrastructure, and integration platforms.
• Challenge weak technical designs and guide teams toward scalable, maintainable, and cost-effective solutions.
• Serve as the SVP's delegate in technical reviews, planning discussions, vendor evaluations, and cross-functional initiatives.
AI, Data & Platform Leadership
• Lead and guide AI, ML, data engineering, analytics, and automation initiatives.
• Help teams move AI ideas from prototype to production-grade systems.
• Provide direction on RAG, agentic systems, LLMOps, reasoning models, model evaluation, data pipelines, data governance, observability, and responsible AI practices.
• Ensure AI and data platforms are reliable, measurable, secure, and aligned to business outcomes.
Engineering Execution
• Work with technology leads to improve delivery discipline, technical quality, and engineering consistency across teams.
• Identify execution risks, technical debt, infrastructure bottlenecks, and unclear ownership.
• Help unblock complex technical and organizational issues.
• Drive measurable improvements in reliability, delivery speed, system performance, and engineering productivity.
People & Technical Leadership
• Coach tech leads, architects, staff engineers, engineering managers, data engineers, ML engineers, and software engineers.
• Raise the technical bar through architecture reviews, mentoring, design critiques, and hands-on guidance.
• Support hiring, onboarding, technical career growth, and succession planning.
• Identify and develop future technical leaders.
Infrastructure, Cost & Operational Efficiency
• Partner with cloud, DevOps, infrastructure, and finance teams to manage technology cost and platform efficiency.
• Review cloud spend, tooling, licensing, vendor usage, and build-versus-buy decisions.
• Drive cost optimization without compromising scalability, security, reliability, or product quality.
• Establish KPIs around system reliability, infrastructure efficiency, AI quality, platform reuse, and engineering effectiveness.
• Other duties as assigned.
Required Qualifications
• Bachelor's degree in software engineering or a related technical field.
• 10+ years of experience in software engineering, architecture, data engineering, AI/ML, cloud infrastructure, or platform engineering.
• 5+ years leading technical teams, architects, engineering managers, or senior engineers, preferably in a global environment.
• Strong technical depth across modern software architecture, APIs, distributed systems, cloud platforms, DevOps, and data platforms.
• Hands-on understanding of AI/ML systems, including LLMs, reasoning models, multimodal models, embeddings, rerankers, RAG, agent workflows, model evaluation, and production deployment.
• Experience evaluating when to use general-purpose LLMs, smaller task-specific models, reasoning models, and traditional ML models based on accuracy, latency, cost, risk, and business value.
• Familiarity with prompt engineering, structured outputs, tool/function calling, agentic workflows, model orchestration, hallucination reduction, and LLM observability.
• Ability to guide teams on LLM selection, inference cost optimization, model safety, guardrails, data privacy, and production deployment.
• Understanding of emerging AI architecture patterns, including multi-agent systems, reasoning loops, retrieval-augmented generation, memory, model routing, and human-in-the-loop review.
• Experience with data engineering, analytics platforms, lakehouse/warehouse architecture, data quality, and governance.
• Strong judgment in technical decision-making, cost tradeoffs, risk management, and execution planning.
• Ability to influence senior technical leaders without needing direct authority over every team.
• Strong communication skills with executives, product leaders, engineers, and business stakeholders
• Ability to lead, decide, coach and execute and represent the SVP as required.
Preferred Qualifications
• Experience with AWS, Azure, Databricks, Snowflake, Kubernetes, Kafka, Spark, MLflow, Terraform, CI/CD, and observability platforms.
• Experience building production systems using OpenAI, Anthropic Claude, Amazon Bedrock, Azure OpenAI, Databricks Foundation Models, or open-source models such as Llama and Mistral.
• Experience with LLMOps, including prompt/version control, evals-as-code, regression testing, model monitoring, AI quality metrics, and cost/latency tracking.
• Experience building production AI platforms, enterprise RAG systems, agentic workflows, or AI governance programs.
• Strong software engineering background in Python, Java, C#, TypeScript, or similar.
• Experience with DDD, CQRS, event-driven architecture, microservices, clean architecture, and platform engineering.

Experience in enterprise SaaS, fintech, nonprofit/association software, or regulated industries.

#LI-MH1 #momentivesoftware

Medical, Dental & Vision Benefits

401(k) Savings Plan with Company Match

Flexible Planned Paid Time Off

Generous Sick Leave

Inclusive & Welcoming Environment

Purpose-Driven Culture

Work-Life Balance

Commitment to Community Involvement

Employer-Paid Parental Leave

Employer-Paid Short-Term Disability

Remote Work Flexibility