AI/ML Developer

Responsible for designing, building, and deploying machine learning models and AI-driven systems within the Google Cloud ecosystem. This role bridges data science and software engineering, focusing on creating scalable, production-ready AI solutions—such as Generative AI, natural language processing, and predictive models—using tools like Vertex AI, TensorFlow, and BigQuery. Model Development & Training: Develop and train predictive and generative AI models using Python and frameworks such as TensorFlow, PyTorch, or Scikit-learn, often within Vertex AI. GCP Implementation: Implement solutions using GCP services like BigQuery, Dataflow, Cloud Functions, and Vertex AI Pipelines to build scalable infrastructure. MLOps and Automation: Design and automate MLOps pipelines (training, deployment, monitoring) to ensure model performance, scalability, and reliability. Data Engineering: Construct data pipelines for ingestion, preprocessing, and storage of structured/unstructured data using SQL and BigQuery. Generative AI Integration: Implement LLMs, retrieval-augmented generation (RAG) patterns, and agentic workflows (e.g., using LangChain). Optimization & Troubleshooting: Monitor and optimize deployed models for accuracy, latency, and cost-effectiveness. Experience: 5+ years in AI/ML model deployment and software engineering. Technical Proficiencies: Strong programming skills in Python and SQL. GCP Expertise: Proven experience with Google Cloud Platform, specifically Vertex AI, Dataflow, and BigQuery. ML Frameworks: In-depth knowledge of TensorFlow, PyTorch, or Scikit-learn. DevOps/Containerization: Proficiency with Docker, Kubernetes (GKE), and CI/CD tools. Education: Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or a related field. GCP Professional Machine Learning Engineer certification. Experience with Vertex AI agent builder Background in Natural Language Processing (NLP) or Computer Vision Key Responsibilities Required Skills and Qualifications Preferred Qualifications