Forward Deployed ML/AI Engineer

Responsibilities: Bridge the gap between AI research and production-grade applications by designing and deploying end-to-end intelligent systems. Act as a strategic partner to customers, translating business objectives into technical AI specifications and scalable infrastructure.

Requirements: Requires 6+ years of ML/SWE experience with a track record of productionizing models and 3+ years of customer-facing implementation experience. Proficiency in both classical ML and GenAI architectures, along with full-stack development and MLOps skills, is essential.

Key skills: Classical Machine Learning, Generative AI, Large Language Models, RAG Pipelines, Python, FastAPI, Flask, Streamlit, Docker, CI/CD, MLOps, Cloud Infrastructure, SQL, NoSQL, Stakeholder Management, API Design

Keywords: Machine Learning, Artificial Intelligence, LLM, RAG, Vector Databases, Scikit-Learn, XGBoost, MLflow, Weights & Biases, Databricks, AWS, GCP, Azure, ETL, Feature Stores, Quantization, Agentic Workflows, Model Registry, Inference Optimization, Full-Stack AI