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:

1. LangGraph (for agent orchestration and reasoning loops)

2. LangChain (for building multi-agent workflows and tool integrations)

3. DSPy or similar declarative frameworks for self-improving agents

4. 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.