AI Forward Deployed Engineer

Visteon Corporation; Chennai; On-site; INR 1,800,000 - 2,800,000; Full time

Job summary

Visteon is seeking an AI Forward Deployed Engineer who partners with business stakeholders to convert high-value AI opportunities into secure, scalable solutions. You will own from discovery to production deployment, including integration, adoption and ongoing improvement.

You'll combine software engineering, generative AI and ML capabilities to deliver measurable impact, with emphasis on governance, security and scalable architectures in enterprise environments.

Qualifications

• Hands-on software, ML or generative AI development experience; production deployment preferred.
• Ability to translate business needs into technical solutions and communicate with stakeholders.

Responsibilities

• Identify high-value AI opportunities and convert them into secure, scalable AI solutions.
• Own delivery from discovery to production deployment, including integration with enterprise systems.
• Develop rapid prototypes, proofs of concept and production AI solutions with governance and security.

Skills

Python

REST APIs

SQL

Git

PyTorch

TensorFlow

FastAPI

Flask

Docker

CI/CD

Cloud Deployment

Observability

Kubernetes

Qdrant

Milvus

pgvector

FAISS

Redis

OpenTelemetry

Grafana

LangChain

LangGraph

Azure OpenAI

Vector Databases

Education

Bachelor's degree in Computer Science, Engineering, Data Science or related field

Tools

Docker

Kubernetes

PostgreSQL

Redis

OpenTelemetry

Prometheus

Grafana

pgvector

FAISS

LangChain

LangGraph

Azure OpenAI

Job description

Job Description

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.