AI/ML Engineer - Cognitive Architecture / Neuro-Symbolic AI

Role Overview

We are seeking an AI Engineer to design and build intelligent agent systems based on cognitive architecture principles and neuro-symbolic reasoning. You will work on next-generation AI that combines LLMs, reasoning engines, memory systems, and planning modules — moving beyond pure deep learning toward explainable and controllable intelligence.

Key Responsibilities

Cognitive & Agent Architecture

·        Design AI agent frameworks incorporating perception, memory, reasoning, planning, and action loops

·        Implement structured memory (episodic, semantic, working memory) for long-running AI agents

·        Build tool-using agents with multi-step decision making and task decomposition

·        Develop evaluation frameworks for reasoning correctness and controllability

Neuro-Symbolic AI Development

·        Combine neural models (LLMs/transformers) with symbolic reasoning (rules, graphs, constraints)

·        Implement knowledge graphs, ontologies, and rule engines

·        Build reasoning pipelines integrating logic solvers and neural inference

·        Improve explainability, traceability, and reliability of AI outputs

LLM & ML Engineering

·        Build retrieval-augmented systems (RAG), planning chains, and memory-aware prompting

·        Implement fine-tuning, alignment, and evaluation workflows

·        Optimize latency, cost, and reliability of inference pipelines

·        Deploy AI services in scalable production environments

Platform & Collaboration

·        Work closely with product and research teams to convert prototypes into production systems

·        Participate in architecture reviews and experimentation cycles

·        Write clean, testable, and well-documented code

Required Qualifications

·        Strong programming skills in Python

·        Experience building AI/ML or LLM-based applications in production

·        Understanding of reasoning, planning, and agent design patterns

·        Knowledge of search algorithms, graphs, and probabilistic reasoning

·        Experience with distributed systems and APIs

Preferred Qualifications (Highly Desired)

·        Hands-on experience with neuro-symbolic AI approaches

·        Knowledge of cognitive architectures such as ACT-R or Soar

·        Experience with knowledge representation (ontologies, semantic graphs)

·        Familiarity with constraint solvers or logic programming

·        Background in reinforcement learning or planning systems

·        Research publications or open-source contributions in AI agents.

We are proud to be an Equal Employment Opportunity (EEO) and Affirmative Action employer. We at  HL Solutions do not discriminate based on Race, Religion, Color, National origin, Sex, Sexual orientation, Gender identity, Gender expression, Age, and Disability status.