AI Engineer - Engineer

Your key responsibilities

• AI Solution Development: Design, develop, and implement AI-powered applications and services using Python, FastAPI/Flask, and Google VertexAI.
• Model Integration & Fine-tuning: Integrate and fine-tune large language models, specifically the Gemini Model, for various NLP tasks and generative AI applications.
• Conversational AI: Develop and enhance conversational AI agents using Dialogflow CX, focusing on natural language understanding and response generation.
•  Agentic AI & Orchestration: Implement Agentic AI patterns and orchestrate complex AI workflows using ADK and LangGraph to create robust and intelligent systems.
• API Integration: Design and implement seamless API integrations with various internal and external systems to ensure data flow and functionality.
• Cloud Infrastructure & Data Processing: Deploy and manage AI solutions on cloud platforms, leveraging services like GCP CloudRun/GKE, BigQuery, CloudSQL, Cloud Composer, and GCS for robust data processing and infrastructure management.
• Code Quality & Best Practices: Write clean, maintainable, and well-documented code following best practices for software development and AgentOps.
• Testing & Deployment: Develop and execute comprehensive tests for AI solutions and assist in the deployment and monitoring of models in production environments.
• Research & Innovation: Stay current with the latest advancements in AI, machine learning, and natural language processing to propose and implement innovative solutions.
• Collaboration: Work closely with cross-functional teams including product managers, data scientists, and other engineers to deliver high-quality AI products.

Your skills and experience

• Educational Qualification: Bachelor’s, Master’s or PhD in a relevant field such as Computer Science, AI/ML Engineer, Statistics, Mathematics, Engineering.
• Professional Experience: Proven experience (4+ years) in an AI Engineer role, demonstrating successful application of AI skills & techniques to solve real-world problems.
• Programming Proficiency: Strong proficiency in programming languages commonly used in data science, such as Python (with libraries like scikit-learn, pandas, NumPy), SQL etc...
• Technical Skills:
  • Experience with web frameworks like FastAPI or Flask for building APIs.
  • Hands-on experience with Google VertexAI platform.
  • Demonstrated experience working with large language models, particularly the Gemini Model.
  • Proficiency in developing conversational AI solutions using Dialogflow CX.
  • Familiarity with LangGraph or similar frameworks for orchestrating AI agents and workflows.
  • Solid understanding of Agentic AI principles and design patterns.
  • Experience with API design, integration, and management.
• Cloud & Data Technologies:
  • Strong experience with Google Cloud Platform (GCP) services including CloudRun/GKE, BigQuery, and CloudSQL.
  • (Nice to have) Familiarity with infrastructure as code tools like Terraform.
• UI Frameworks (Nice to have): Experience with Svelte, Angular, Django, Streamlit or Taipy for building user interfaces.
• Deployment: Familiarity with AgentOps practices and tools.
• Problem-Solving & Analytical Skills:
  • Excellent analytical and problem-solving abilities with a strong attention to detail.
  • Ability to translate complex business requirements into technical solutions.
• Core Competencies:
  • Strong communication and collaboration skills, with the ability to work effectively in a team environment.
  • Proactive and self-motivated with a passion for learning new technologies.
  • Ability to manage multiple priorities and deliver high-quality work within deadlines.
• Domain Knowledge (Preferred): Experience in the financial services industry, particularly Private Banking, is highly desirable.