Role Summary

We are seeking an experienced Senior AI Developer. This role focuses on the design, development, and deployment of intelligent autonomous systems that can reason, plan, and execute complex tasks. The ideal candidate will build production-grade AI solutions that drive significant innovation and efficiency within our global financial services operations.

Responsibilities

• Architect Autonomous Agents: Design and implement robust, goal-driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK).
• Develop and Evaluate RAG Pipelines: Engineer and optimize end-to-end Retrieval-Augmented (RAG) systems, including data ingestion, chunking strategies, and implementing rigorous pipeline evaluation frameworks for accuracy and performance.
• Fine-Tune & Optimize LLMs: Implement advanced model customization techniques, including PEFT (Parameter-Efficient Fine-Tuning) and QLoRA, to adapt large models for specialized financial tasks.
• Deploy Cloud- AI Solutions: Build, deploy, and manage scalable AI applications on the Google Cloud Platform (GCP), with a strong focus on Vertex AI services.
• Build Scalable Backend Services: Develop secure, high-performance APIs and microservices using Python and FastAPI to integrate agentic systems into the wider enterprise architecture.

Technical Skillset Requirements

Core AI & Frameworks:

• Agentic Frameworks: Expert-level knowledge of agentic frameworks such as LangChain, LangGraph, Google Agent Development Kit (ADK)
• LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3.
• RAG & Vector Databases: Deep expertise in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma.

Software & Cloud Engineering:

• Programming & APIs: Expert-level Python and demonstrable experience building production services with FastAPI.
• Cloud Platform: Mastery of GCP, particularly Vertex AI, Google Kubernetes Engine (GKE), and Cloud Functions.
• Databases: Strong command of relational databases like PostgreSQL and familiarity with NoSQL solutions.
• MLOps & DevOps: Production experience with Docker, Kubernetes, CI/CD pipelines (e.g., Jenkins, GitHub Actions), and Infrastructure as Code (Terraform).

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field.
• 6-8 years of professional, hands-on experience in software engineering, with a primary focus on building and deploying complex AI/ML systems into production.
• A proven track record of delivering scalable, reliable, and high-performance software solutions from concept to deployment.