AI Forward Deployed Engineer
Description
AI Forward Deployed Engineer
Overview of Role:
The AI Forward Deployed Engineer partners directly with business stakeholders to identify high-value opportunities and convert them into secure, scalable and measurable AI solutions. The role combines strong software engineering, generative AI and machine learning capability with stakeholder-facing delivery. It owns the journey from discovery and rapid prototyping through enterprise integration, production deployment, adoption and continuous improvement.
KPIs:
Business Impact: Measured value delivered through AI solutions, including efficiency, quality, revenue, cost or risk outcomes.
Time to Production: Speed and predictability in progressing priority use cases from discovery and proof of concept to production.
Solution Quality and Reliability: Accuracy, relevance, latency, availability, scalability and operational stability of deployed solutions.
Adoption and Stakeholder Satisfaction: Active usage, stakeholder feedback and successful integration into business workflows.
Security and Governance: Adherence to data privacy, access control, responsible AI and enterprise governance requirements.
Reusability and Enablement: Reusable accelerators, reference implementations, documentation and knowledge transfer created for broader adoption.
Key Year One Deliverables:
Opportunity and Current-State Assessment: Assess priority business opportunities, existing AI capabilities, data sources, platforms, integrations and delivery constraints.
Stakeholder Needs Analysis: Define business objectives, user needs, success measures, adoption barriers and technical requirements with key stakeholders.
Prioritized Delivery Roadmap: Create and maintain a roadmap based on business value, feasibility, data readiness, risk and resource needs.
Prototypes and Proofs of Concept: Develop rapid prototypes to validate technical feasibility, user value and solution direction.
Production AI Solutions: Build, deploy and operationalize selected AI solutions with appropriate security, scalability, reliability and support readiness.
Enterprise Integration: Integrate AI capabilities with enterprise applications, identity systems, APIs, data sources and operational workflows.
Evaluation and Observability: Establish evaluation, error-analysis, logging, metrics, tracing, usage and cost-monitoring mechanisms.
Documentation and Enablement: Deliver architecture documentation, runbooks, reusable patterns, training and knowledge transfer for users and support teams.
Qualifications:
Bachelor's degree in Computer Science, Engineering, Data Science or a related technical discipline, or equivalent practical experience.
Hands-on experience developing software, machine learning or generative AI solutions through internships, academic projects or professional environments; production deployment experience is preferred.
Demonstrated ability to translate business requirements into practical technical solutions and communicate effectively with technical and non-technical stakeholders.
Experience and Skills:
Software Engineering: Strong Python programming; backend and API development using FastAPI, Flask REST or equivalent; REST APIs, SQL, relational databases, Git, testing, debugging and code review.
Generative AI and Context Engineering: Large language models, prompt and context engineering, RAG and Graph RAG, embeddings, semantic search, vector databases, tool/function calling, memory, agent orchestration and evaluation.
Machine Learning: ML and deep-learning fundamentals, natural language processing, model evaluation, data preprocessing and experience with PyTorch and/or TensorFlow.
Production Engineering: Containerization, CI/CD, cloud or on-premises deployment, observability, performance tuning, scalability, resilience and production troubleshooting. Familiarity with multi-model architectures, AI gateways, model routing, token and cost management, and model selection is preferred.
Data and Integration: Data pipelines, enterprise application integration, identity and access management, secure configuration and interoperability across systems.
AI Quality and Governance: Evaluation frameworks, error analysis, guardrails, responsible AI, data privacy, security, access control, lifecycle management and usage/cost monitoring.
Forward Deployed Delivery: Stakeholder discovery, problem decomposition, rapid prototyping, ambiguity management, end-to-end ownership, documentation and knowledge transfer.
Good to Have: Microsoft Azure and Azure OpenAI; LangGraph, LangChain or comparable frameworks; Qdrant, Milvus, pgvector or FAISS; Docker, Kubernetes, PostgreSQL and Redis; OpenTelemetry, Prometheus and Grafana; LiteLLM or comparable AI gateways.
Key Behaviors:
Customer and Business Focus: Builds trusted stakeholder relationships and keeps solutions anchored to measurable user and business outcomes.
Ownership: Takes responsibility from discovery through production stabilization, adoption and continuous improvement.
Problem Solving: Uses structured analysis and hands-on troubleshooting across application, model, data, integration and infrastructure layers.
Agility and Adaptability: Works effectively with ambiguity, changing priorities and evolving AI technologies.
Collaboration and Communication: Explains complex topics clearly and works effectively across Product, Engineering, Architecture, Infrastructure, Security and business teams.
Continuous Learning: Actively evaluates new approaches while applying sound engineering judgment, governance and evidence-based decision-making.
Attention to Detail: Maintains high standards for quality, security, documentation and operational readiness.
Reporting Structure:
This role reports to the Innovation department lead.
Visteon Culture
If you thrive in a fast-paced organizational culture that requires agility, adaptability and a growth mindset to stay ahead of the curve, Visteon is the place. We value high performance and a drive for results. Innovation, risk-taking and continuous learning help us keep pace with the ever-changing landscape of our industry and remain market leaders. At Visteon, you can be more.
Skills
- AI
- API
- Azure
- CI/CD
- Cloud
- Containerization
- Data Pipelines
- Data Science
- Deep Learning
- Docker
- Embeddings
- FAISS
- FastAPI
- Flask
- Generative AI
- Git
- Grafana
- IAM
- Kubernetes
- LangChain
- LangGraph
- LiteLLM
- LLM
- Machine Learning
- Milvus
- Model Evaluation
- NLP
- Observability
- OpenAI
- OpenTelemetry
- pgvector
- PostgreSQL
- Prometheus
- Proof of Concept
- Prototyping
- Python
- PyTorch
- Qdrant
- RDBMS
- Redis
- REST
- Semantic Search
- SQL
- TensorFlow
- Vector Databases