Role overview
We are seeking a talented and motivated
AI/ML Engineer
with a strong foundation in
large language models (LLMs)
,
NLP
, and
graph analytics
to join our growing AI/ML division.
You will work across the stack — from data pipelines to distributed training to full-stack applications — and directly contribute to solving complex real-world problems.
This role is ideal for a candidate passionate about applying machine learning to cybersecurity, language technologies, and secure systems modeling.
What you'll work on
- Fine-tune and deploy large language models (LLMs) using distributed systems and cloud platforms (AWS SageMaker, ECS).
- Develop and optimize Retrieval-Augmented Generation (RAG) pipelines for structured and unstructured data.
- Build full-stack applications using TypeScript , React , and Next.js to support AI/ML products.
- Conduct graph analytics and implement pattern-of-life modeling for anomaly detection and cybersecurity applications.
- Design and manage PostgreSQL schemas and data pipelines (e.g., Apache NiFi) for ingestion into Elasticsearch.
- Collaborate with cross-functional teams to operationalize machine learning models into production environments.
*Qualifications:
Required:**
- Bachelor’s degree or higher in Computer Science, Mathematics, Data Science, or related fields (Master’s preferred).
- 2+ years of professional experience (including internships/research assistantships) in machine learning, NLP, or AI.
- Hands-on experience fine-tuning LLMs with frameworks like PyTorch , Hugging Face , or JAX .
- Proficiency in Python and TypeScript programming.
- Experience building or contributing to full-stack applications with React/Next.js .
- Strong grasp of distributed systems and use of cloud platforms (AWS preferred).
- Solid knowledge of SQL and data modeling.
- Experience working with graph databases (Neo4j) or graph-based machine learning.
What we're looking for
- Strong analytical, problem-solving, and communication skills.
- Proactive and able to work independently in a remote or hybrid setting.
- Passion for learning and applying new AI/ML techniques to real-world problems.
Tags & focus areas
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