VAM Systems is currently looking for Data Scientists/ ML Engineer- (AI/ML Specialists) (On-Site) for our Bahrain operations with the following skillsets and terms & conditions:
Years of Experience: 7 10 years
Qualification
Bachelors 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.
Job Responsibility:
Design and develop machine learning models to support AI-driven banking solutions Collaborate with data engineers to access and prepare data for modeling Apply statistical and ML techniques to solve business problems (e.g. churn prediction credit scoring) Evaluate model performance and optimize for accuracy precision and recall Deploy models into production using MLOps frameworks and CI/CD pipelines Ensure models are explainable auditable and compliant with regulatory standards Work with business stakeholders to identify AI opportunities and define success metrics Document model assumptions data sources and performance benchmarks.
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)
Joining time frame: (15 - 30 days)
Remote Work :
No
Employment Type :
Full-time
VAM Systems is a Business Consulting, Technology Solutions and Professional Services organization working with major organizations in USA, UAE, Bahrain, India, Singapore and Australia. Delivers leading edge information and communication technology based business solutions to enabl ... View more