Principal Cybersecurity Architect

Providence is seeking an exceptional Principal Cybersecurity Architect to lead the research, development, and deployment of Small Language Models (SLMs) specialized for healthcare and cybersecurity applications. This role combines cutting edge machine learning research with practical implementation, requiring deep expertise in model architecture design, advanced fine tuning techniques, graph-based knowledge systems, and vector-based retrieval augmentation.

The ideal candidate will architect and train efficient, domain-specialized language models using state-of-the-art techniques including LoRA/QLoRA, graph neural networks, vector embeddings, and hybrid retrieval systems. This position requires both depth in machine learning and hands-on implementation skills to deliver high-impact solutions for healthcare operations, clinical workflows, and security applications.

Key Responsibilities

- Design, develop, and optimize Small Language Models for healthcare and cybersecurity domains using advanced architectures (efficient transformers, MoE, sparse attention), compression techniques (quantization: INT8/INT4/GPTQ/AWQ, pruning, knowledge distillation), and emerging SLM architectures (Phi-3, Gemma, Mistral 7B variants) with rigorous ablation studies balancing model size, inference speed, accuracy, and resource requirements
- Implement parameter-efficient fine-tuning (PEFT) techniques including LoRA, QLoRA, prefix tuning, prompt tuning, and adapter layers; design instruction tuning pipelines aligning SLMs with clinical terminology and security protocols; develop multi-task learning frameworks; and deploy continuous learning strategies using DPO, RLHF, and active learning while optimizing hyperparameters to prevent overfitting on limited healthcare datasets
- Implement knowledge graphs capturing domain relationships and build graph-augmented language models integrating Neo4j/Cosmos DB with unstructured text processing, leveraging GNN components (PyTorch Geometric, DGL, GraphSAGE) for entity relationship modeling and complex graph traversal reasoning




- Create entity extraction and relation classification pipelines that automatically populate and maintain knowledge graphs from clinical documents, security logs, and operational data to enable graph-based reasoning across patient journeys, care coordination workflows, and security incident chains
- Optimize model inference using vLLM, TensorRT-LLM, llama.cpp, GGML, and Optimum for maximum throughput/minimum latency; implement scalable model serving architectures (Triton, TorchServe, FastAPI) with batching, caching, and load balancing; design A/B testing frameworks and champion-challenger patterns for secure production model updates
- Design and implement vector database architectures for semantic search and RAG applications using specialized embeddings optimized for cyber text, with hybrid search combining dense vector retrieval, sparse BM25 matching, and graph-based context expansion
- Build advanced RAG architectures implementing multi-vector retrieval, reranking strategies (Cohere, Cross-Encoders), context compression, and query decomposition; optimize vector indexing (HNSW, IVF, Product Quantization) balancing accuracy, latency, and memory footprint; and develop chunking pipelines for diverse cyber data formats
- Experience in end-to-end data pipelines for cyber text corpora processing, implementing data quality frameworks through de-identification and synthetic data generation (Synthea), bias detection, and representativeness metrics while building experiment tracking systems (MLflow) for versioning and reproducibility
- Develop distributed training workflows using DeepSpeed, FSDP, or Megatron-LM for efficient multi-GPU/multi-node training on Azure ML; create automated data augmentation pipelines (back-translation,



paraphrasing, entity substitution, synthetic generation) to expand limited healthcare training datasets while maintaining clinical validity
- Design comprehensive evaluation frameworks measuring healthcare-specific task performance (NER, relation extraction, summarization, QA, reasoning) with custom benchmark datasets aligned to clinical accuracy requirements, regulatory compliance, and operational impact; implement automated robustness testing for adversarial attacks, edge cases, OOD detection, and hallucination detection
- Conduct comparative analysis of SLM variants across accuracy, latency, throughput, memory, and cost metrics; build interpretability tools using attention visualization, SHAP values, and integrated gradients to ensure clinical safety and explainability for healthcare stakeholders and regulatory requirements
- Understand monitoring and observability dashboards tracking model performance drift, data distribution shifts, embedding quality degradation, and retrieval accuracy in production; collaborate with MLOps teams on containerization (Docker), Kubernetes/AKS deployment, and CI/CD pipelines for automated testing and deployment workflows
- Build security-focused SLMs for threat detection, log analysis, vulnerability assessment, incident response automation, and policy compliance checking
- Partner with clinical stakeholders, security teams, and product managers to identify high-impact use cases and translate domain requirements into technical specifications; mentor junior data scientists on SLM development, fine-tuning techniques, and healthcare AI; contribute to knowledge sharing through documentation, tech talks, and research communities; stay current with emerging research through literature review, conference participation, and experimentation

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Principal Cybersecurity Architect (Telangana)
🏢 Providence College of Engineering
📍 Telangana