Ml Engineer

Role Overview Seeking an experienced ML Engineer to design, build, fine-tune, and productionize classical ML and GenAI solutions. You will own end-to-end deliveryfrom problem framing and data preparation to deployment, monitoring, and iteration—primarily on Google Cloud (Vertex AI, BigQuery). The role requires hands-on experience with RAG architectures, LLM prompt engineering, and robust MLOps practices. You will collaborate directly with customers, translate requirements into secure, scalable architectures, and lead project execution. This is a night-shift position aligned to 8am–5pm EST. Required Qualifications 5-7 years overall industry experience with 5+ years of core software engineering building secure, scalable, performant applications. 3+ years hands-on experience designing, building, and deploying ML applications in production. Proficiency in Python and standard data science libraries: NumPy, Pandas, Scikit-learn. Hands-on experience with ML frameworks: TensorFlow or PyTorch; plus XGBoost. Strong foundation in at least one deep learning area: NLP, Computer Vision, or related. Proven experience building production-grade RAG systems and engineering prompts for LLMs. Cloud experience on GCP, including Vertex AI and BigQuery; building ML pipelines and serving endpoints. MLOps expertise: CI/CD for ML, model registry/versioning, monitoring, logging, data/Concept drift detection, alerting, and rollback. Data and feature engineering: data cleaning, handling outliers and class imbalance, understanding distributions, and creating high-quality features. Excellent communication and customer-facing collaboration skills; ability to take ownership and lead delivery. Willingness and availability to work night shift aligned to 8am–5pm EST. Preferred certifications: Google Cloud Professional Machine Learning Engineer, Google Cloud Generative AI Engineer, TensorFlow Developer. Preferred: Experience integrating ML pipelines with existing data processing pipelines. Responsibilities Lead end-to-end ML development: problem framing, data preparation, model training/fine-tuning, evaluation, deployment, and post-production support. Design, implement, and optimize RAG architectures (chunking, embeddings, retrieval, ranking) and iterate on prompts to improve LLM response accuracy. Build, automate, and monitor scalable ML pipelines on GCP (Vertex AI Pipelines, BigQuery, Cloud Storage) and establish CI/CD and model registry/versioning. Implement observability for models: performance monitoring, data and concept drift detection, alerting, and remediation playbooks. Collaborate with customers and engineering teams to translate requirements into secure, performant, and cost-effective cloud architectures; document decisions and designs. Integrate ML workflows with data processing pipelines and platform tooling as needed. Provide night-shift support aligned to 8am–5pm EST for customer-facing engagements and production operations.