Sr / Midle AI Engineer

Department:
Artificial Intelligence & Core Engineering
Position Level:
Mid-Level / Senior AI Engineer
Employment Type:
Full-Time / Contract
About CyberDX
At CyberDX, we are building next-generation intelligent systems that bridge advanced artificial intelligence, distributed architecture, and secure processing. We specialize in high-performance machine learning workflows, privacy-first edge deployments, and contextual automation. We are looking for an ambitious
AI Engineer
with deep expertise in optimizing models for
Edge Devices
and designing low-latency
NLP-driven Recommendation Systems
to join our core technical team.
Position Summary
We are seeking a versatile Mid-to-Senior AI Engineer who can bridge the gap between heavy machine learning research and constrained production environments. In this role, you will architect, optimize, and deploy state-of-the-art NLP models and personalization/recommendation engines designed to run efficiently at the edge (on-device or localized hardware) with strict constraints on latency, footprint, and data security.
Key Responsibilities
Edge AI & Model Optimization:
Design, compress, and optimize heavy deep learning and NLP models (e.g., Transformers, BERT variants, small language models) for deployment on resource-constrained edge hardware.
Quantization & Acceleration:
Apply advanced optimization frameworks such as model quantization (INT8/FP16), pruning, knowledge distillation, and ONNX runtime / TensorRT conversion to minimize latency and memory overhead.
NLP-Powered Recommendation Systems:
Architect, train, and scale contextual recommendation pipelines driven by Natural Language Processing (semantic matching, content-based filtering, hybrid recommenders) to deliver real-time suggestions.
On-Device Inference & Pipelines:
Build efficient C++/Python inference pipelines that execute locally on edge nodes, preserving data privacy and operating seamlessly under intermittent network connectivity.
Performance & Scalability Tuning:
Profile model performance, memory footprints, and throughput on target edge hardware; troubleshoot bottlenecks to achieve real-time response targets.
Cross-Functional Collaboration:
Partner with backend, data engineering, and product teams to integrate edge modules cleanly into larger software systems and secure deployment pipelines.
Required Qualifications & Skills
Experience:
Mid-Level:
3-5 years of professional software engineering experience with at least 2-3 years focused on production-grade machine learning / NLP systems.
Senior:
5+ years of software/AI engineering experience, including a strong track record of shipping end-to-end edge AI or complex recommender systems to production.
Core Technical Stack:
Proficient in
Python
and
C++
(essential for high-performance edge deployment and runtime integration).
Deep working knowledge of machine learning frameworks such as
PyTorch
or
TensorFlow
.
Edge & Optimization Expertise:
Hands-on experience with model compression toolchains:
ONNX, TensorRT, CoreML, TensorFlow Lite (TFLite), or OpenVINO
.
Deep understanding of memory management, hardware acceleration (CPU/GPU/NPU), and latency reduction strategies for edge devices.
NLP & Recommendation Systems:
Strong mastery of NLP pipelines (tokenization, embeddings, vector search, semantic retrieval, and fine-tuning lightweight transformer architectures).
Experience designing recommendation systems (collaborative filtering, deep learning-based recommenders, multi-stage retrieval, and ranking algorithms).
Software Engineering Best Practices:
Solid grasp of containerization (
Docker
), version control (
Git
), CI/CD pipelines, and RESTful API development (FastAPI/Flask).
Preferred Qualifications
Experience with vector databases (e.g., Milvus, Faiss, Chroma) for semantic recommendation lookups.
Familiarity with Retrieval-Augmented Generation (RAG) frameworks or lightweight LLM deployment on local hardware.
Background in cybersecurity principles, data privacy compliance, or secure code delivery practices.
What We Offer
Cutting-Edge Tech Stack:
Work on challenging problems at the intersection of Edge computing, NLP, and distributed architecture.
High Impact:
Your code and architectural decisions will directly shape core products utilized across our enterprise ecosystem.
Growth & Autonomy:
A collaborative culture that encourages technical exploration, continuous learning, and ownership.