Artificial Intelligence Engineer - Machine Learning
First seen by Alion on Jul 25, 2026.
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.
Skills
Artificial Intelligence, Machine Learning, Agentic AI, Intelligent Processes Automation, LangChain, LangGraph, Data Analytics