Forward Deployment Engineer

Talentstra; United States; On-site; USD 100,000 - 130,000; Full time

Job summary

A technology firm in the United States is seeking a Forward Deployment Engineer (FDE) who has strong AI/ML experience. This role involves working closely with enterprise customers to tackle complex engineering challenges and drive product innovation. Candidates should have hands-on experience with LLMs, proficiency in Python, and familiarity with related software tools. The ideal candidate thrives in fast-paced environments and enjoys solving dynamic technical problems.

Qualifications

• Strong proficiency in Python is mandatory.
• Hands-on experience with LLMs, RAG architectures, or prompt engineering.
• Understanding of vector databases and embeddings.

Responsibilities

• Work at the intersection of engineering, customer success, and product innovation.
• Solve complex technical challenges directly with enterprise customers.

Skills

AI/ML experience

Hands-on experience with LLMs

Proficiency in Python

Experience with REST APIs

Microservices architecture familiarity

Tools

TensorFlow

PyTorch

LangChain

Hugging Face

Docker

Kubernetes

Job description

We are looking for a Forward Deployment Engineer (FDE) with strong AI/ML experience to work at the intersection of engineering, customer success, and product innovation. This role is ideal for someone who thrives in dynamic environments, enjoys solving complex technical challenges, and is comfortable working directly with enterprise customers

Required Technical Skills

• Hands‑on experience with LLMs, RAG architectures, fine‑tuning, or prompt engineering.
• Experience with frameworks such as TensorFlow, PyTorch, LangChain, Hugging Face.
• Understanding of vector databases and embeddings (e.g., Pinecone, FAISS, Weaviate).

Software Engineering

• Strong proficiency in Python (mandatory).
• Experience with REST APIs, FastAPI/Flask.
• Familiarity with microservices architecture.
• Good understanding of data structures, algorithms, and system design.
• Experience with AWS/Azure/GCP deployments.
• CI/CD pipelines and infrastructure as code.
• Containerization and orchestration (Docker, Kubernetes).
• ETL pipelines and streaming systems.
• SQL/NoSQL databases.
• Data preprocessing and feature engineering.