AIML Specialists (Data Scientists ML Engineer)
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موقع الوظيفة:
الراتب شهرياً:
تاريخ النشر:
نُشرت قبل 19 ساعة
عدد الوظائف الشاغرة:
1 عدد الوظائف الشاغرة
ملخص الوظيفة
VAM Systems is currently looking for AI/ML Specialists (Data Scientists/ ML Engineer) (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)
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.
Conversational UX / Design
Conversation design frameworks (Google CCAI Microsoft Bot Framework Voiceflow Botpress)
Flow design and intent management (Dialogflow Rasa Cognigy)
Tone empathy and personality design for AI personas
A/B testing dialogue variants and measuring user satisfaction.
Data & Infrastructure
Data pipelines (Airflow dbt Kafka)
Structured/unstructured data ingestion (PDFs databases APIs)
Feature store and model registry management (MLflow Kubeflow)
Vector database deployment and optimization
Monitoring logging and drift detection.
Governance Security & Compliance
Model explainability (SHAP LIME)
Bias/fairness audits and data privacy
Compliance with GDPR ISO 42001 NIST AI RMF and local banking regulations
Secure prompt logging and audit trails.
Products & Strategy
Translating business problems into AI use cases
Roadmapping and budget planning
KPI design (accuracy user satisfaction automation ROI)
Vendor management (OpenAI Anthropic AWS etc.)
Change management and user adoption
Joining time frame: (15 - 30 days)
The selected candidates shall join VAM Systems Bahrain and shall be deputed to one of the leading banks in Bahrain.
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)
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.
Conversational UX / Design
Conversation design frameworks (Google CCAI Microsoft Bot Framework Voiceflow Botpress)
Flow design and intent management (Dialogflow Rasa Cognigy)
Tone empathy and personality design for AI personas
A/B testing dialogue variants and measuring user satisfaction.
Data & Infrastructure
Data pipelines (Airflow dbt Kafka)
Structured/unstructured data ingestion (PDFs databases APIs)
Feature store and model registry management (MLflow Kubeflow)
Vector database deployment and optimization
Monitoring logging and drift detection.
Governance Security & Compliance
Model explainability (SHAP LIME)
Bias/fairness audits and data privacy
Compliance with GDPR ISO 42001 NIST AI RMF and local banking regulations
Secure prompt logging and audit trails.
Products & Strategy
Translating business problems into AI use cases
Roadmapping and budget planning
KPI design (accuracy user satisfaction automation ROI)
Vendor management (OpenAI Anthropic AWS etc.)
Change management and user adoption
Joining time frame: (15 - 30 days)
The selected candidates shall join VAM Systems Bahrain and shall be deputed to one of the leading banks in Bahrain.
المجال
مزودو خدمة الإنترنت / أجهزة التجزئة الكهربائية والإلكترونية والأجهزة
المهارات المطلوبة
- Apache Hive
- S3
- Hadoop
- Redshift
- Spark
- AWS
- Apache Pig
- NoSQL
- البيانات الضخمة
- مستودع البيانات
- Kafka
- Scala
