Hybrid AI Data Platform Lead

General Motors LLC

Nacogdoches (TX)

Hybrid

USD 180,000 - 240,000

Full time

4 days ago

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Job summary

General Motors LLC seeks an AI Data Engineering Manager to lead a multidisciplinary team delivering industrialized data assets, AI data products, and data platforms supporting BI, analytics, and generative AI use cases. You will own a distinct AI data engineering area, translate strategy into a team roadmap, and ensure trusted, scalable, secure data products.

You will steer architecture, data contracts, MLOps patterns, and enterprise patterns while partnering with executives across GPSC Insights

Qualifications

Bachelor’s degree in Computer Science, Software Engineering, Data Engineering, Data Science, or a related field, or equivalent experience.
10+ years of experience in data engineering, software engineering, platform engineering, AI/ML engineering, or a related discipline, including production delivery of scalable data solutions.
3+ years of experience directly leading technical teams, including responsibility for coaching, performance, staffing, prioritization, and delivery outcomes.
Advanced understanding of data and AI platform architecture, including data pipelines, data products, feature or embedding pipelines, model data delivery, and production operations.
Strong technical proficiency in Python and SQL, with working knowledge of distributed data processing such as Apache Spark and container orchestration such as Kubernetes.
Experience with streaming and event-driven data solutions using Kafka or comparable technologies, including operation in Kubernetes-based environments.
Experience with cloud platforms; Azure preferred, with AWS or GCP also considered.
Demonstrated experience establishing engineering standards for CI/CD, infrastructure automation, testing, observability, incident response, security, and production support.
Experience with data architecture, modeling, schema design, data contracts, relational and non-relational data stores, object storage, APIs, and data governance.
Working knowledge of machine learning and generative AI data lifecycle requirements, including training/evaluation data, feature engineering, embeddings, retrieval, inference, monitoring, and data/model drift.
Experience setting objectives, managing roadmaps, allocating resources, tracking KPIs, communicating status and risks, and delivering results across multiple teams or departments.
Experience leading teams that deliver AI applications or data products using AI development frameworks and services such as LangChain, CrewAI, Vertex AI, or comparable technologies.
Working knowledge of RAG/vector search, FastAPI/React application patterns, LLM evaluation, and agent orchestration.
Strong communication, influencing, conflict-resolution, and negotiation skills, with the ability to adapt recommendations for technical, business, and executive audiences.

Responsibilities

Lead and develop a diverse team of data engineers, AI/ML platform engineers, software engineers, and technical leads; establish clear roles, objectives, priorities, and measures of success.
Translate GPSC and Data organization strategies into an actionable AI data engineering roadmap, operating plan, delivery commitments, and team-level objectives and key performance indicators.
Own the architecture, delivery, and operational health of scalable data and AI engineering capabilities across batch, streaming, and near-real-time workloads.
Guide the team in assembling, curating, and transforming structured, semi-structured, unstructured, and multimodal datasets for model training, validation, evaluation, feature generation, embedding generation, retrieval, and inference.
Set technical direction and engineering standards for data contracts, schema management, lineage, metadata, data quality, observability, security, privacy, responsible AI, and platform reliability.
Drive adoption of repeatable MLOps/LLMOps patterns, including data and model versioning, experiment and evaluation workflows, CI/CD, deployment automation, monitoring, drift detection, and retraining triggers.
Set technical direction for AI application development, retrieval, evaluation, and orchestration capabilities, ensuring the team applies appropriate enterprise patterns.
Establish policies, processes, and reusable frameworks for source-system integrations from files, database extracts, APIs, event streams, external suppliers, and supply-chain systems.
Plan and allocate people, capacity, technology, and budget across the team’s portfolio; reprioritize and reallocate resources as business needs, risks, and opportunities change.
Drive strategy and implementation of large projects that impact multiple departments, balancing performance, scalability, reliability, resiliency, security, privacy, cost, and SLA/SLO attainment.
Make and communicate key technical decisions within the managed area; resolve competing demands, sensitive situations, conflicts, and tradeoffs within the organization and with partner teams.
Build strong relationships with senior business and technology partners, data scientists, ML engineers, product owners, platform teams, cybersecurity, governance, suppliers, and vendors; use persuasion and negotiation to gain alignment on new approaches.
Oversee production support, incident response, root-cause analysis, and preventative improvements for data and AI platforms; ensure the team meets commitments and addresses gaps when results fall short.
Establish mechanisms to understand customer and partner needs, use data and insights to prioritize work, and raise risks or issues that could negatively affect business outcomes or customer experience.
Lead change management and bounded experimentation to evaluate emerging AI, data, and engineering technologies while protecting enterprise standards and operational stability.
Build the team’s capability through hiring, staffing, career discussions, coaching, succession planning, performance management, recognition, and targeted development of technical and leadership skills.
Create an inclusive, accountable, and psychologically safe environment where employees share ideas, challenge assumptions respectfully, learn from setbacks, and own outcomes.
Model GM behaviors by speaking candidly about risks, protecting sensitive information, holding partners to ethical standards, moving with urgency, collaborating as one team, and considering enterprise impact in decisions.