Gen AI Engineer - Google ADK

Role: Gen AI Engineer
Location: Dallas, TX - Onsite

Key Responsibilities:

• 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.

Required Skill Set:

Technical Skills:

• 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.