VP of Research & Development

Position Overview

We are seeking a VP of Research & Development to lead the transformation of our engineering organization into an AI-first, cloud-native delivery engine. This executive will drive the transition from a traditional software development lifecycle (SDLC) to a modern AI Development Lifecycle (AI-DLC) — embedding ML pipelines, intelligent automation, and data-driven decision-making into every stage of product development on Azure cloud infrastructure.

This role combines strategic leadership with hands-on technical direction. You will own the R&D vision, architect scalable AI/ML systems, align technology investments with business outcomes, and build a high-performance team capable of executing at speed while maintaining trust, security, and reliability. The ideal candidate brings deep expertise in MLOps, cloud-native architecture, and GenAI integration — not just as concepts, but as production realities.

Roles & Responsibilities

AI-DLC Transformation & Innovation

• Lead the transition from SDLC to AI-DLC, integrating data pipelines, model development, evaluation, deployment, monitoring, and iteration into core engineering workflows
• Design and implement end-to-end ML pipeline orchestration using Azure ML, MLflow, or Kubeflow — from feature engineering through model serving
• Establish MLOps practices: model versioning, automated retraining triggers, A/B testing of models in production, drift detection, and performance monitoring
• Define AI-DLC quality gating: automated checkpoints at each lifecycle stage (data validation, model evaluation, deployment readiness)
• Implement continuous evaluation loops — production model performance monitoring against ground truth with automated rollback triggers
• Build AI testing frameworks: unit testing for models, integration testing for pipelines, adversarial testing for robustness
• Architect RAG pipeline designs including chunking strategies, embedding model selection, retrieval optimization, and reranking
• Establish agentic AI design patterns — multi-agent orchestration, tool use, and autonomous workflow execution
• Define prompt management systems: version-controlled prompt libraries, A/B testing of prompts in production
• Lead fine-tuning vs. RAG vs. in-context learning decision frameworks — knowing when each approach fits
• Implement LLM evaluation frameworks: hallucination detection, factual grounding, response quality scoring
• Design guardrails and content filtering: input/output validation, toxicity detection, PII redaction in LLM responses
• Drive synthetic data generation strategies for training and testing where production data is restricted
• Architect vector database solutions and semantic search implementation (Pinecone, Azure AI Search, Weaviate)
• Apply model compression and optimization for edge deployment — quantization, distillation, pruning
• Optimize token cost and inference scaling strategies across AI workloads

Technology Strategy & Cloud Architecture

• In collaboration with Product, define and execute the long-term technology roadmap aligned to AI-native delivery
• Architect Azure cloud-native infrastructure: AKS (Kubernetes), Azure Functions, Cosmos DB, Azure AI Services, Azure OpenAI Service
• Drive Infrastructure as Code (Terraform, Bicep) and GitOps deployment models
• Design API-first architecture and microservices patterns for scalable, real-time, AI-enabled platforms
• Implement data pipeline architecture — ETL/ELT modernization from batch-heavy to event-driven, real-time enrichment using Azure Event Hubs / Kafka
• Apply data mesh principles: domain-owned data products, federated governance, self-serve data infrastructure
• Establish feature stores for ML — centralized, versioned, reusable feature engineering (Feast, Azure ML feature store)
• Build data quality frameworks: automated schema validation, anomaly detection, lineage tracking
• Design multi-region deployment strategies for global mobility use cases (latency, data residency, failover)
• Lead edge computing and IoT integration strategies relevant to the company's device and hardware footprint
• Implement FinOps discipline: cost modeling for AI workloads, GPU compute optimization, spot instance strategies, reserved capacity planning
• Drive multi-modal AI capabilities — vision, document understanding, speech-to-text integration

Product & Business Alignment

• Partner with Product, Sales, and Customer Success to deliver impactful, customer-centric AI-powered solutions
• Translate global mobility use cases into scalable technical solutions with measurable business impact
• Align R&D priorities with company growth targets and market opportunities
• Lead build vs. buy vs. partner evaluation frameworks specifically for AI capabilities

Governance, Security & Trust

• Implement AI governance frameworks covering privacy, compliance, and model risk management
• Apply Zero Trust security architecture to AI workloads — identity-based access, secrets management (Azure Key Vault), network segmentation
• Establish model risk management aligned to regulatory expectations (EU AI Act awareness, SOC 2 implications)
• Build AI audit trails: decision logging, reproducibility, explainability-on-demand for regulated use cases
• Deploy responsible AI tooling: explainability (SHAP, LIME), bias detection, model cards
• Implement ethical AI review checkpoints before production deployment
• Conduct third-party AI vendor risk assessments — evaluating external models, APIs, and data providers for security, reliability, and IP exposure
• Define IP strategy for AI outputs: ownership of model weights, generated content, and training data derivatives
• Manage governance of training data — provenance tracking, PII handling, consent management, right-to-be-forgotten compliance

Leadership & Team Development

• Build and lead a multidisciplinary team spanning software engineering, data engineering, ML engineering, embedded systems, and AI/ML research
• Foster a culture of innovation, accountability, ownership, and continuous improvement
• Recruit and develop top-tier technical talent with AI/ML depth
• Drive cross-functional sprint models — data engineers, ML engineers, product, and domain experts operating as one unit, not silos
• Launch AI-assisted development tooling adoption across R&D (Copilot, Claude Code, automated code review)
• Baseline engineering team AI fluency and build structured upskilling programs
• Promote inner-source practices: shared libraries, reusable components, internal API marketplaces

Operational Excellence

• Improve delivery speed, reliability, and product quality through CI/CD automation, platform engineering, and observability
• Implement SRE practices: SLOs, error budgets, incident management frameworks
• Establish DORA metrics as the engineering performance framework (deployment frequency, lead time, change failure rate, MTTR)
• Deploy observability stack: distributed tracing, structured logging, metrics dashboards
• Implement feature flagging, canary deployments, and progressive rollout strategies
• Prioritize developer experience (DX) as a first-class engineering investment
• Quantify and manage technical debt — measure it, prioritize it, allocate capacity against it
• Build Azure DevOps or GitHub Actions pipelines purpose-built for ML workflows (train-test-deploy automation)
• Manage R&D budgets, vendors, and external development partners
• Implement blameless postmortem culture and learning loops tied to engineering OKRs

Strategic Growth

• Identify emerging technologies, partnerships, and acquisition opportunities aligned to AI-first strategy
• Contribute to executive strategy, long-term innovation planning, and board-level technology discussions
• Lead vendor consolidation reviews to rationalize AI/ML toolchain spend

Success Measures (First 6 Months)

• DORA metrics baselined by Day 30; improvement targets set and tracked by Day 90
• Azure cloud migration/optimization roadmap delivered within 60 days
• ML pipeline operational within 90 days with at least one model in production serving
• MLOps maturity assessment completed by Day 45 with gap-to-target roadmap
• AI governance framework documented and adopted across R&D by Month 4
• At least two GenAI-powered features shipped to customers within 6 months
• Data architecture modernization plan delivered within 90 days with executive sign-off
• Engineering team AI fluency baseline measured and upskilling program launched by Month 2
• Vendor consolidation review completed — AI/ML toolchain spend rationalized within first quarter
• Strengthened engineering culture with measurable reduction in technical debt

Experience & Qualifications

Required

• 15+ years of experience in software engineering, R&D, or product development with progressive technical leadership
• 5+ years in a senior leadership role leading cross-functional technical teams including ML/AI practitioners
• Direct experience building and scaling ML/AI pipelines in production environments (not just POCs)
• Hands-on expertise with Azure cloud platform (strongly preferred), including PaaS/IaaS architecture decisions, AKS, Azure AI Services
• Working knowledge of MLOps toolchains: model registries, experiment tracking, automated retraining, drift detection
• Experience with containerization (Docker, Kubernetes) and cloud-native deployment patterns
• Strong expertise in system architecture, API design, and microservices patterns
• Track record improving engineering performance through modern development practices (CI/CD, observability, DORA metrics)
• Excellent executive communication, stakeholder alignment, and board-level presentation skills
• Comfortable operating at both strategic and hands-on levels — including architecture reviews, code reviews, and technical decision-making