Artificial Intelligence Engineer

Role Overview

We are seeking a highly motivated AI/ML Engineer with 4–8 years of experience to join our technology team. This role will focus on designing, developing, and deploying AI-driven automation solutions for enterprise operations in the hedge fund administration space. The ideal candidate will have hands-on expertise in Agentic AI frameworks, large-scale data analysis, and building self-healing, self-optimizing systems. You will work in a fast-paced environment, contributing to multiple projects simultaneously, with a strong emphasis on efficiency, accuracy, and systems that continuously improve through reflection and reasoning loops.

Key Responsibilities

• AI-Driven Automation: Design and implement intelligent systems to automate complex operational workflows (e.g., document processing, vendor payments).
• Agentic AI Development: Build and refine agent-based AI systems capable of reasoning, reflection, and self-correction to improve accuracy and reliability.
• Data Analysis & Modeling: Analyze large, diverse datasets to uncover insights, train models, and optimize performance.
• Self-Healing Systems: Develop mechanisms for systems to detect errors, auto-correct, and optimize performance without manual intervention.
• Enterprise Integration: Collaborate with business and technology teams to integrate AI solutions seamlessly into enterprise applications and processes.
• Continuous Improvement: Monitor deployed models, implement feedback loops, and ensure systems evolve with changing business needs.
• Cross-Functional Collaboration: Partner with product managers, operations teams, and senior leadership to deliver AI solutions aligned with strategic goals.
• Documentation & Best Practices: Maintain clear technical documentation and promote best practices in AI/ML development and deployment.

Skills and Competencies

Core AI/ML Expertise

• Strong experience with machine learning, deep learning, and natural language processing.
• Hands-on knowledge of Agentic AI frameworks such as:
• LangGraph (for agent orchestration and reasoning loops)
• LangChain (for building multi-agent workflows and tool integrations)
• DSPy or similar declarative frameworks for self-improving agents
• Exposure to AutoGPT-style architectures or other autonomous agent systems
• Experience with large-scale data pipelines and distributed computing.

Technical Proficiency

• Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn).
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices.
• Exposure to reinforcement learning, self-optimizing algorithms, or autonomous agents.
• Experience with vector databases (e.g., Pinecone, Weaviate, FAISS) for knowledge retrieval.
• Understanding of prompt engineering and fine-tuning LLMs for enterprise use cases.

Enterprise Automation Experience

• Prior work in automating enterprise operations (e.g., finance, accounting, document workflows).
• Experience with unstructured data extraction (PDFs, emails, multilingual documents).
• Familiarity with OCR, NLP pipelines, and document intelligence systems.

Problem-Solving & Innovation

• Ability to design systems that learn from errors and improve autonomously.
• Strong analytical and reasoning skills for complex problem-solving.

Collaboration & Delivery

• Comfortable working on multiple projects in a high-pace environment.
• Strong communication skills to explain technical concepts to non-technical stakeholders.
• Ability to balance innovation with practical delivery timelines.

• Seniority Level
• Mid-Senior level
• Industry
• Financial Services
• Internet Marketplace Platforms
• Software Development
• Employment Type
• Full-time
• Job Functions
• Engineering
• Skills
• LangChain
• LangGraph
• Python (Programming Language)
• Self-healing
• TensorFlow
• PyTorch
• Agentic AI Development
• Vector Databases
• Natural Language Processing (NLP)
• Pinecone.io