AI Forward Deployed Engineer
Visteon Corporation2 days ago
Kolkāta, India +3 moreEntry Level
Responsibilities
- Assess business opportunities, current AI capabilities, data sources, platforms, integrations, risks, and delivery constraints.
- Define stakeholder objectives, user needs, success measures, adoption barriers, and technical requirements.
- Create and maintain a prioritized AI delivery roadmap based on value, feasibility, data readiness, risk, and resources.
- Develop rapid prototypes and proofs of concept to validate technical feasibility and user value.
- Build, deploy, operationalize, and stabilize secure, scalable, and reliable AI solutions in production.
- Integrate AI capabilities with enterprise applications, identity systems, APIs, data sources, and operational workflows.
- Establish evaluation, error analysis, logging, metrics, tracing, usage monitoring, and cost-monitoring mechanisms.
- Produce architecture documentation, runbooks, reusable patterns, training, and knowledge transfer for users and support teams.
- Partner with technical and non-technical stakeholders and own delivery from discovery through adoption and continuous improvement.
Requirements
- 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.
- Strong Python programming and backend/API development experience using FastAPI, Flask REST, or equivalent technologies.
- Experience with REST APIs, SQL, relational databases, Git, testing, debugging, and code review.
- Knowledge of large language models, prompt and context engineering, RAG, Graph RAG, embeddings, semantic search, vector databases, tool or function calling, memory, agent orchestration, and evaluation.
- Understanding of machine learning, deep learning, natural language processing, model evaluation, and data preprocessing, with experience using PyTorch and/or TensorFlow.
- Experience with containerization, cloud or on-premises deployment, observability, performance tuning, scalability, resilience, and production troubleshooting.
- Familiarity with data pipelines, enterprise application integration, identity and access management, secure configuration, interoperability, AI quality, governance, data privacy, and responsible AI.
- Ability to translate business requirements into practical technical solutions and communicate effectively with technical and non-technical stakeholders.
- Preferred experience includes multi-model architectures, AI gateways, model routing, token and cost management, model selection, and production deployment.
Tech Stack
Categories
Forward Deployed