Deputy Manager - Application

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.