Unit Head (Artificial Intelligence- Digital Banking)

Job Description

Title: Unit Head (Artificial Intelligence- Digital Banking)

Company Name: Mutual Trust Bank PLC

Vacancy: --

Age: Na

Job Location: Dhaka

Salary: --

Experience:

• At least 7 years
• The applicants should have experience in the following business area(s): Banks

• Masters

• Minimum Master’s degree from a reputed university. Special preference will be given to applicants having Master’s in Computer Science, Data Science, AI, Applied Mathematics, or Quantitative Finance. 

• At least 7 years
• The applicants should have experience in the following business area(s): Banks

• Having a portfolio of past and ongoing AI initiatives having significant, proven impacts.

• Hands On experience in LLM and NLP development.

• Deep understanding of: Tokenization, Embeddings, Transformers, Sequence modeling, Language modeling, LLM fine-tuning (SFT, LoRA, PEFT), Prompt engineering, Evaluation of downstream models, Traditional NLP (regex, CRFs, SVMs, feature-based models), LangChain, AutoGen, CrewAI, LlamaIndex, LLM inference optimizations, Embedding models & VectorDBs, Ranking algorithms, Time-series models.

• End-to-end pipeline experience (research → deployment → monitoring).

• Skilled in: Tool calling, Multi-agent orchestration, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), Reasoning and planning frameworks, Building tools for APIs, DBs, and web interaction. 

• Design, develop, and maintain LLM-based AI components, improving efficiency for internal teams and user-facing applications.

• Build systems spanning data retrieval, RAG, vector search, chat interfaces, and agentic workflows.

• Experiment with and evaluate new LLM architectures, frameworks, and AI/ML toolchains.

• Enhance data infrastructure to support high-volume LLM usage, embeddings, and scalable storage.

• Collaborate with cross-functional teams (analysts, SWE, operations, product) to define use cases and deliver AI-powered solutions.

• Contribute to the company`s technical vision for generative AI foundations.

• Conduct quantitative evaluations, benchmarking, and performance optimization of NLP/LLM models.

• Integrate NLP and AI components into large-scale production systems, ensuring reliability and low latency.

• Develop and maintain text data generation, preprocessing, and training/evaluation pipelines.

• Follow best practices in software engineering, testing (TDD), CI/CD, and version control.

• Stay updated with the latest LLM advancements (RAG, MCP, Tool Use, ReAct, agent systems).

• Build end-to-end NLP/LLM applications from research → deployment → monitoring.