Senior Machine Learning Engineer (GCP)

Tiger Analytics is looking for a skilled and innovative Machine Learning Engineer with hands-on experience in Google Cloud Platform (GCP) and Vertex AI to design, build, and deploy scalable ML solutions. You will play a key role in operationalizing machine learning models and driving the end-to-end ML lifecycle, from data ingestion to model serving and monitoring. Key Responsibilities: - Develop, train, and optimize ML models using Vertex AI, including Vertex Pipelines, AutoML, and custom model training. - Design and build scalable ML pipelines for feature engineering, training, evaluation, and deployment. - Deploy models to production using Vertex AI endpoints and integrate with downstream applications or APIs. - Collaborate with data scientists, data engineers, and MLOps teams to enable reproducible and reliable ML workflows. - Monitor model performance and set up alerting, retraining triggers, and drift detection mechanisms. - Utilize GCP services such as BigQuery, Dataflow, Cloud Functions, Pub/Sub, and GCS in ML workflows. - Apply CI/CD principles to ML models using Vertex AI Pipelines, Cloud Build, and GitOps practices. - Implement model governance, versioning, explainability, and security best practices within Vertex AI. - Document architecture decisions, workflows, and model lifecycle clearly for internal stakeholders. Requirements 1. Advanced Generative AI - Advanced RAG including Graph based hybrid retrieval - Multimodal agent - Deep knowledge on ADK , Langchain Agentic Frameworks - Fine tuning and Distillation 2. Python Expertise - Expert in Python with strong OOP and functional programming skills - Proficient in ML/DL libraries: TensorFlow, PyTorch, scikit-learn, pandas, NumPy, PySpark - Experience with production-grade code, testing, and performance optimization 3. GCP Cloud Architecture & Services - Proficiency in GCP services such as: - Vertex AI - BigQuery - Cloud Storage - Cloud Run - Cloud Functions - Pub/Sub - Dataproc - Dataflow - Understanding of IAM, VPC 6. API Development & Integration - Designs and builds RESTful APIs using FastAPI or Flask - Integrates ML models into APIs for real-time inference - Implements authentication, logging, and performance optimization 7. System Design & Scalability - Designs end-to-end AI systems with scalability and fault tolerance in mind - Hands-on experience in developing distributed systems, microservices, and asynchronous processing Benefits This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility. Originally posted on Himalayas