Data Scientists - Banking at VAM Systems

Manama, Capital, Bahrain -

Full Time

Start Date

Immediate

Expiry Date

30 Mar, 26

Salary

0.0

Posted On

30 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, MLOps, AI in Financial Services, Python, TensorFlow, AWS, Data Preprocessing, Feature Engineering, Cloud-Based ML Platforms, Model Evaluation, NLP Engineering, RESTful APIs, Containerization, Microservices, Agent Orchestration

Industry

IT Services and IT Consulting

Description

Job Description VAM Systems is currently looking for Data Scientists - Banking for our Bahrain operations with the following skillsets and terms & conditions: Years of Experience: 7 – 10 years Qualification Bachelor’s Degree in Computer Science / Engineering Preferably BE Computer Science & Engineering Professional Training Required: Machine Learning, Deep Learning, MLOps, AI in Financial Services. Professional Qualification Required: Google Professional ML Engineer, Microsoft AI Engineer Associate Professional Licenses Required Not applicable. Professional Certifications Required: TensorFlow Developer Certificate, AWS Certified Machine Learning. Must-Have: •Proven hands-on delivery experience in banking, financial institutions, or insurance within Gen AI solutions such as chatbots, document analysis, etc., leveraging RAG and robust architecture with proper governance and security measures •Several years of ML experience with implemented use cases. •Hands-on work experience most of which in banking, financial institutions, or insurance industries. Experience required: Ability to build and deploy ML models using Python and relevant libraries. Understanding of supervised and unsupervised learning algorithms. Experience with model evaluation and performance metrics. Familiarity with AI use cases in banking (e.g., fraud detection, personalization) Knowledge of data preprocessing and feature engineering. Ability to work with cloud-based ML platforms (e.g., Azure ML, AWS SageMaker). Understanding of MLOps and model lifecycle management. Ability to communicate insights and build explainable AI models. Core AI / NLP Engineering • Python (PyTorch, TensorFlow, LangChain, Hugging Face, OpenAI API, Anthropic Claude, etc.) • LLM fine-tuning (LoRA, PEFT, prompt tuning) • Retrieval-Augmented Generation (RAG), vector databases (Pinecone, FAISS, Weaviate, Chroma) • Prompt engineering and orchestration (LangChain, LlamaIndex, Semantic Kernel, DSPy) • Knowledge of embeddings, tokenization, and transformer architecture • Cloud AI tools: AWS Bedrock, Azure OpenAI, Vertex AI, OpenSearch, ElasticSearch •Model evaluation: hallucination detection, grounding, and benchmarking (BLEU, ROUGE, TruthfulQA, etc.) Software Engineering & Backend Integration •RESTful and GraphQL APIs, webhooks • Containerization and deployment (Docker, Kubernetes, CI/CD) • Authentication and user/session management • Data pipelines and microservices • Knowledge of frameworks like FastAPI, Flask, NestJS, or Express • Integration with enterprise data (SharePoint, Salesforce, SQL, internal APIs) Agent Orchestration & Tooling • LangGraph, AutoGen, CrewAI, Flowise, or similar agent frameworks • Task-decomposition and reasoning chains • Function calling, tool use, and API chaining • Memory design (short-term vs long-term) • Context management and grounding strategies. Joining time frame: (15 - 30 days)

Responsibilities

The Data Scientist will be responsible for delivering AI solutions in banking, focusing on Gen AI applications such as chatbots and document analysis. They will also build and deploy machine learning models while ensuring proper governance and security measures.