AI Architect

Panasonic Automotive•UNAVAILABLE, UNAVAILABLE

8d•Hybrid

About The Position

AI Architect will lead the design, development, and governance of next-generation artificial intelligence systems across our enterprise. As AI rapidly evolves from isolated ML models to autonomous, multi-agent ecosystems, this role sits at the forefront of that transformation. The AI Architect will own PASA’s enterprise AI architecture — spanning generative AI, agentic AI frameworks, LLM orchestration, MLOps pipelines, and responsible AI governance — while translating cutting-edge capabilities into real-world automotive applications including connected vehicles, in-vehicle infotainment (IVI), ADAS, manufacturing automation, and supply chain intelligence.

Requirements

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or related technical field; Master’s or Ph.D. preferred.
10+ years of experience in software engineering, data science, or solutions architecture; 4+ years in a dedicated AI/ML architecture or senior AI engineering leadership role.
Strong experience designing and delivering cloud-based data and AI platforms on Microsoft Azure, Amazon Web Services, or Google Cloud Platform, with proven expertise in building scalable architectures, implementing HA/DR strategies, and optimizing cost and performance for large-scale workloads
Demonstrated experience designing and deploying enterprise-scale AI/ML systems in production — not just prototypes.
Deep expertise in LLM architecture, prompt engineering, context engineering, retrieval-augmented generation (RAG), and fine-tuning workflows.
Hands-on experience with multi-agent frameworks: LangChain / LangGraph, CrewAI, AutoGen, or OpenAI Agents SDK
Experience working with leading LLMs including OpenAI GPT-4o, Meta Llama 3/4, Google Gemini, Anthropic Claude, or Mistral.
Strong understanding of agentic AI cognitive architecture: planning loops, memory management (short-term, long-term, episodic), tool use, and goal decomposition.
Experience designing guardrails and safety systems for autonomous agentic workflows operating without continuous human intervention
Familiarity with vector databases and embedding stores (e.g., Pinecone, Weaviate, Chroma, pgvector).
Strong experience with cloud AI/ML platforms: AWS SageMaker, Azure Machine Learning / Azure OpenAI Service, or Google Vertex AI
Expertise in MLOps and LLMOps practices and tooling: MLflow, Kubeflow, Weights & Biases, CI/CD pipelines for ML
Strong Python programming skills; proficiency in SQL and familiarity with Spark for large-scale data processing
Experience with containerization and orchestration: Docker, Kubernetes, Helm.
Knowledge of real-time data streaming and event-driven architectures (Apache Kafka, Spark Streaming).
Deep knowledge of responsible AI principles: model explainability (SHAP, LIME), bias detection and mitigation, fairness frameworks, and model risk management.
Familiarity with AI regulatory frameworks: EU AI Act, NIST AI Risk Management Framework (RMF), ISO/IEC 42001 AI Management Systems.
Experience defining enterprise AI governance policies, audit trails, data privacy controls (GDPR, CCPA), and ethical AI deployment standards
Ability to design agentic AI governance structures: accountability frameworks for autonomous agents, human-in-the-loop controls, and fail-safe mechanisms.

Responsibilities

Architect and govern PASA’s enterprise AI strategy, covering generative AI, agentic/multi-agent systems, LLM orchestration, computer vision, and predictive analytics.
Design multi-agent AI architectures — defining cognitive components including reasoning engines, memory systems (short-term, long-term, episodic), tool interfaces, and goal management loops.
Lead the selection, evaluation, and integration of modern AI platforms, LLMs (e.g., GPT-4o, Llama 3, Gemini, Claude), agentic frameworks (LangChain, LangGraph, CrewAI, AutoGen, OpenAI Agents SDK), and vector databases.
Define and maintain reusable AI architecture blueprints, including RAG (Retrieval-Augmented Generation) patterns, prompt engineering standards, context engineering practices, and fine-tuning pipelines.
Build and scale MLOps and LLMOps pipelines supporting continuous model development, evaluation, deployment, monitoring, and retraining in production environments.
Establish and enforce responsible AI governance frameworks: model explainability, bias detection and mitigation, guardrails design, AI safety protocols, audit trails, and regulatory compliance (EU AI Act, NIST AI RMF).
Define KPIs and evaluation frameworks to measure AI solution accuracy, reliability, ethical performance, and business impact.
Partner with automotive engineering teams to deliver AI-driven use cases across connected vehicles, ADAS, in-vehicle infotainment (IVI), manufacturing quality control, and supply chain optimization.
Architect edge AI and embedded inference solutions for low-latency, on-device AI processing in automotive environments.
Collaborate cross-functionally with data scientists, software engineers, product managers, and business stakeholders to translate requirements into scalable, production-ready AI solutions.
Develop and maintain PASA’s enterprise data strategy for AI: data pipelines, knowledge graphs, vector stores, and real-time streaming architectures that keep AI agents and models contextually current.
Mentor and grow a high-performing team of AI/ML engineers and data scientists; champion a culture of innovation, responsible AI, and continuous learning.
Represent PASA at industry conferences, standards bodies, and ecosystem partnerships; bring cutting-edge research and emerging technologies back to the organization.
Design and implement scalable, secure, and cost-optimized AI/ML architectures on cloud platforms such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform, including data lakes/lakehouses, distributed compute, networking, and high availability/disaster recovery patterns.
Architect end-to-end AI platforms by integrating data engineering, MLOps, and deployment pipelines using tools like Apache Spark, Kubernetes, and Azure Machine Learning, with strong focus on security, governance, and performance optimization.