ML/AI Engineer (LLM Production)

1. Core Technical Areas

• LLM Fine-Tuning & RAG: Fine-tune open-weight language models on domain-specific data using techniques such as SFT/Quantization. Build and optimize Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and policy document ingestion.
• Inference Optimization: Serve models efficiently using production inference engines (e.g., vLLM, SGLang). Apply quantization and batching strategies to meet strict latency and throughput SLAs.
• MLOps & Deployment: Manage model deployment pipelines across DEV, UAT, PRE-PRD, and PRD environments on enterprise cloud infrastructure (IBM watsonx.ai / OpenShift).

1. AI-Assisted Development

• Utilize modern LLMs to accelerate development - Python code generation, prompt engineering, and pipeline optimiz
• Engage in prompt engineering to refine how systems interact with complex, multilingual datasets.

1. Research & Prototyping

• Evaluate emerging open-source models, inference frameworks, and AI libraries for production feasibility.
• Produce written validation reports and contribute to technical design and test documentation.

• Broad Exposure: You will understand how LLM fine-tuning, RAG, inference optimization, and deployment pipelines interact in a real-world production system.
• Technical Guidance: Work directly with senior engineers to learn how to move AI models from notebook experiments to production-ready, enterprise-grade code.
• Impactful Work: Your contributions will directly power a live AI system handling real financial data at scale.

Technical Requirements:

• Degree in Computer Science, Data Science, AI, or a related field.
• Hands-on experience with LLM fine-tuning, RAG pipelines, or model serving.
• Strong proficiency in Python.
• Solid understanding of machine learning fundamentals and deep learning frameworks (PyTorch, TensorFlow).
• Familiarity with relevant libraries such as Hugging Face Transformers (e.g. Qwen/Deepseek), LangChain, LlamaIndex, or vLLM.
• Ability to read and write technical documentation in English.
• Proficient in utilizing cutting-edge AI tools (e.g. Claude Code, GPT-codex) to accelerate development cycles and conduct rapid feasibility studies (PoCs).

Nice-to-Haves:

• Experience with LoRA, QLoRA, Unsloth, DPO, or RLHF fine-tuning techniques.
• Familiarity with quantization (INT4, INT8) and production inference optimiz
• Experience with vector databases (e.g., Milvus, pgvector).
• Exposure to IBM watsonx.ai, OpenShift, or Kubernetes.
• Experience with multilingual NLP, particularly CJK (Chinese, Japanese, Korean) datasets.
• Prior hands-on experience with AI projects in a financial services or regulated industry context.

• Flexible working hours and work-from-home policy.
• Subsidized access to premium AI development tools to empower your workflow.
• On-job training and technical guidance.
• Opportunity to work on a high-impact, production LLM system in the financial services sector.
• Exposure to a cutting-edge open-weight model stack and enterprise-grade deployment practices.