AI Forward Deployed Engineer

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.