Forward Deployed AI Engineer

Job Description: Forward Deployed Engineer (AI/ML Focus)

Position Overview:

We are seeking a high-caliber “Forward Deployed AI Engineer (FDE)” to embed directly with our key customers and deploy production-grade AI solutions. FDE will be an  elite technical execution arm on the front lines. You will deeply understand a client’s business challenges, architect bespoke AI/ML workflows using our platform, and write the critical integration code to make those models functional, secure, and scalable within their environment.

The ideal candidate bridges the gap between deep AI/LLM engineering and client-facing product strategy, possessing the grit to debug a complex system.

Key Responsibilities:

AI Integration & Delivery: Own the end-to-end implementation of AI/ML models and Generative AI workflows directly into customer tech stacks and enterprise workflows.
Bespoke AI Engineering: Build custom data pipelines, implement Retrieval-Augmented Generation (RAG) systems, optimize prompt chains, and fine-tune models to fit specific client data and use cases.
Infrastructure & Deployment: Deploy heavy AI workloads onto client infrastructure (Cloud or Hybrid/On-Premise), ensuring data privacy, low-latency inference, and cost-effective compute usage.
Technical Advisory: Act as the trusted AI consultant for client engineering teams, guiding them on data readiness, model evaluation metrics, and AI security protocols.
Feedback Loop Optimization: Surface real-world customer edge cases, model drift, and feature gaps back to our internal Core AI Research and Product squads to improve the foundational platform.

Technical Competencies:

AI & Generative AI Ecosystem: Hands-on experience working with LLM APIs (OpenAI, Anthropic, open-source models via Hugging Face), prompt engineering frameworks (LangChain, LlamaIndex), and vector databases (Pinecone, Milvus, Qdrant).
Machine Learning Engineering: Strong proficiency in Python and standard ML libraries (NumPy, Pandas, Scikit-Learn). Understanding of deep learning frameworks (PyTorch or TensorFlow) is highly valued.
Data Architecture & ETL: Deep experience building robust data pipelines, processing unstructured data, and writing complex SQL/NoSQL queries to fuel AI training and context injection.
Cloud & MLOps Infrastructure: Strong grasp of cloud computing (AWS, Azure, or GCP), containerization (Docker, Kubernetes), and serving models in production (e.g., Triton, FastAPI, vLLM).

Domain & Soft Skills:

Client-Facing Grit: Outstanding communication and presentation skills. Ability to confidently command a room, whether aligning with client data scientists or presenting ROI to C-level executives.
Extreme Adaptability: Comfortable with ambiguity, shifting priorities, and diving into chaotic, undocumented client tech stacks to make AI solutions work.
Ethical & Secure AI Focus: Strong understanding of data compliance, enterprise privacy constraints, and mitigating AI risks (hallucinations, bias, data leakage).

Experience & Education:

Experience: 4+ years of professional software engineering experience, with at least 2 years dedicated to building, deploying, or integrating AI/ML and NLP systems. Prior experience in technical consulting or enterprise deployment is a major plus.
Education: Bachelor’s or Master’s degree in Computer Science or Information technology, Data Science, Artificial Intelligence, or equivalent practical technical experience.