AI Engineer

We are looking for a pragmatic and results-driven AI Solutions Architect to lead the integration of Generative AI and advanced automation into our department's workflows. Unlike a research-heavy role, this position focuses on the Application Layer: building high-impact tools for intelligent market analysis, automating complex decision-making processes, and architecting the roadmap for our AI evolution. You will be the bridge between cutting-edge LLM capabilities and practical business efficiency, starting as a key individual contributor with the potential to shape our future AI strategy.

Key Responsibilities:

• Intelligent Analysis Systems: Design and deploy RAG (Retrieval-Augmented Generation) frameworks to transform internal reports, market news, and unstructured data into actionable insights for the trading and risk teams.
• Workflow Automation: Develop AI Agents and autonomous workflows to streamline repetitive high-value tasks, moving from manual processes to "human-in-the-loop" automated systems.
• AI Application Development: Build and maintain the middleware/orchestration layer (using frameworks like LangChain or LlamaIndex) to connect LLMs with our internal databases and APIs.
• Model Optimization: Evaluate and implement fine-tuning strategies for open-source models (e.g., Llama 3, DeepSeek) when off-the-shelf APIs do not meet specific accuracy or privacy requirements.
• Strategic Roadmap: Develop a technical AI roadmap that identifies high-ROI opportunities within the department and outlines the transition from pilot projects to scalable production systems.

Technical Qualifications:

• Core AI Engineering: 3+ years of experience in AI application development with a strong focus on Large Language Models (LLMs) and Natural Language Processing (NLP).
• The Orchestration Stack: Proficiency in Python and deep experience with orchestration frameworks such as LangChain, LlamaIndex, or Haystack.
• Automation Mastery: Proven track record of building Agentic Workflows (e.g., AutoGPT, CrewAI) and integrating them into production environments.
• Data & Infrastructure: Familiarity with Vector Databases (e.g., Pinecone, Milvus, Weaviate) and experience handling API integrations (RESTful, GraphQL).
• Model Layer (Bonus): Practical experience in Parameter-Efficient Fine-Tuning (PEFT/LoRA) and local model deployment (Ollama, vLLM).

Preferred Attributes:

• Strategic Thinking: Ability to distinguish between AI "hype" and actual business value, prioritizing projects based on feasibility and impact.
• Collaborative Mindset: Effective at working across teams (IT, Risk, Trading) to understand domain-specific pain points.
• Self-Starter: Comfortable working independently to build prototypes while maintaining a vision for long-term scalability.