Gen AI Engineer - Google ADK

• Machine Learning Model Development: Research, design, and implement advanced machine learning algorithms and models to address business challenges.
• Data Engineering: Extract, transform, and load (ETL) large datasets from various sources into a Postgres database.
• Model Deployment: Deploy machine learning models into production environments, ensuring optimal performance and scalability.
• AI Integration: Integrate AI capabilities into existing systems and applications.
• Agentic AI / GenAI: Build and integrate agentic AI and RAG-based applications using Python and frameworks such as LangChain and Google ADK, including tool calling and multi-agent workflows.
• Document AI / IDP: Build and tune models for document classification, OCR post-processing, layout-aware key-value and table extraction, and LLM-based extraction, with confidence scoring and a human-in-the-loop feedback loop.
• Team Leadership: Mentor and guide junior team members in machine learning and data science practices.
• Problem Solving: Analyze complex business problems and develop innovative solutions using machine learning techniques.
• Collaboration: Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.

Artificial Intelligence (AI), Machine Learning, Algorithms, Business Model, Data Sets, PostgreSQL, Database Extract Transform and Load (ETL), Production Systems, Artificial Intelligence (AI), Python Programming/Scripting Language, * Machine Learning: Strong proficiency in machine learning algorithms (regression, classification, clustering, deep learning) and their implementation using Python libraries (TensorFlow, PyTorch, Scikit-learn).

• AI: Understanding of AI concepts, including natural language processing (NLP), computer vision, and reinforcement learning.
• Generative AI and Agentic AI: Hands-on experience with RAG, LangChain, Google ADK, and building agentic workflows (tool calling, multi-agent orchestration) in Python.
• Programming: Expert-level Python programming skills, including data manipulation, statistical analysis, and model development.
• Database: Proficiency in SQL and experience with PostgreSQL for data storage and retrieval.
• Cloud Platforms: Familiarity with cloud platforms (AWS, GCP, Azure) for deploying and managing machine learning workloads.
• Data Engineering: Experience with data pipelines, ETL processes, and data preprocessing techniques.