AI ML - LLM Specialist

a) Identify open-source or licensed models (LLMs/SLMs) based on use cases and in line to available MSIL infrastructure. Proactievely assess the need of additional infrastructure and prepare business case for procurement.
b) Design and execute fine-tuning pipelines using industry standard techniques like LoRA or PEFT to adapt models to domain-specific enterprise data, focusing on tasks such as natural language understanding, code generation, and query augmentation for .NET/PL/SQL/Java/Python interactions. c) Handle data preparation: Clean, anonymize, and preprocess large-scale organizational datasets for training, incorporating privacy-preserving methods like differential privacy.
d) Handle data preparation: Clean, anonymize, and preprocess large-scale organizational datasets (e.g., from Oracle databases) for training, incorporating privacy-preserving methods like differential privacy.
e) Develop and maintain deployment scripts using frameworks like PyTorch, integrating with serving tools or vLLM for scalable API endpoints. f) Prototype LLM integrations with enterprise apps, such as generating PL/SQL queries from natural language inputs or enhancing .NET services with AI-driven recommendations. g) Implement evaluation metrics (e.g., BLEU, ROUGE, or custom domain benchmarks) to measure fine-tuned model performance and iterate based on feedback. h) Document processes, create reusable codebases, and mentor future team members as the AI team scales. Competencies / Skills a) 4+ years of hands-on experience in AI/ML engineering, with at least 2 years focused on LLMs or generative AI. b) In-Depth knowledge of Cloud native technologies and tools c) Proven track record of fine-tuning and deploying models in production environments, ideally on-premise or edge devices. Familiarity with GPU-accelerated computing: CUDA, NVIDIA tools, and resource management. d) Experience with enterprise data pipelines, including handling sensitive datasets from relational databases. e) Good Understanding of common systems integration methods and technologies including APIs, Web Services, JSON, XSD, XML, message driven and event driven systems. API development for ML serving: REST/gRPC endpoints, with basic understanding of integration patterns (e.g., for .NET apps). g) Excellent verbal and communication skills with the ability to work effectively in cross-functional teams.