Generative AI Fabric Engineering Professional

Congruent is seeking to support Generative AI Fabric development through its engineering team. The company s engineers ( Vendor Personnel ) should have deep expertise in machine learning, large language models, advanced deep learning, data engineering, prompt optimization, and model fine-tuning. Familiarity with PyTorch or TensorFlow and experience deploying AI solutions on AWS, Azure, or GCP are essential. The team must also demonstrate a strong understanding of ethical AI practices, including bias mitigation, fairness, and regulatory compliance.

• Design and build production-grade Generative AI systems, including LLM, RAG, and agent-based applications.
• Develop robust data pipelines, evaluation frameworks, and ensure system reliability.
• Optimize model architecture and inference/training stacks for performance and cost efficiency.
• Manage large-scale OpenSearch clusters and build ingestion pipelines for high-quality indexing and retrieval.
• Create reusable solution patterns and reference implementations.
• Troubleshoot issues across the ML lifecycle: data preparation, training, fine-tuning, evaluation, and serving.
• Collaborate with product management, research, and engineering teams, present at events and conferences.
• Sprint-based goals to deliver software enhancements and bug fixes for 360E services.
• Ongoing support for the 360E customer base to resolve issues and determine implementation needs.
• Other support as determined in application release roadmap.

• BS/MS in Computer Science or related field; 10+ years of experience in distributed/cloud software development.
• Exceptional written and verbal communication skills.
• Demonstrated experience designing, implementing, and operating scalable AI/LLM systems in production.
• Hands-on expertise with LLM and Generative AI techniques, including parameter-efficient fine-tuning (e.g., LoRA/QLoRA), instruction tuning, and advanced prompt strategies.
• Practical experience with RAG architectures, vector search, and related tooling (OpenSearch, vector databases, PostgreSQL, Kafka).
• Proficiency in Python and shell scripting.
• Experience with LLM frameworks and LLMOps tools (LangChain, LlamaIndex, MLflow, KServe, Kubeflow, Triton).
• Proven ability to design data collection/annotation workflows and systematic evaluation for production quality.
• Strong publication record in top-tier venues is a plus.

• Advanced degree (MS/PhD) preferred.
• Expertise in RAG solution architecture and knowledge stores (OpenSearch + vector stores).
• Experience delivering Generative AI and agent solutions.
• Familiarity with multimodal modeling and modern computer vision.
• Experience with LLM serving technologies (e.g., DeepSpeed, FasterTransformer).
• Public cloud experience; strong understanding of IaaS/PaaS and competitive cloud capabilities.
• Experience with fine-tuning (PEFT) and multi-task serving techniques.

Deep understanding of Transformers, training methods, and optimizers; frameworks: PyTorch, JAX, or TensorFlow.