AI / Machine Learning Engineer
VAM Systems
Employer Active
Posted 2 hrs ago
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Experience
7 - 23 Years
Education
Bachelor of Technology/Engineering, Master of Laws (LLM)(Law)
Nationality
Any Nationality
Gender
Not Mentioned
Vacancy
1 Vacancy
Job Description
Roles & Responsibilities
Job Location
Bahrain
Experience
7 to 23 years
Qualification Level
Graduate; MBA / Management Post Graduates
Job Function
Banking / Financial Services
IT - Software
Skillset
AI//ML, TensorFlow, Python, AWS,Azure, Banking
Preferred Jobseekers
Jobseekers from any country
VAM Systems is a Business Consulting, IT Solutions and Services company.
VAM Systems is currently looking for AI / Machine Learning Engineer (On-Site) 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.
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.
Machine Learning / Deep Learning.
Statistics and Finance knowledge.
Data Preprocessing & Feature Engineering.
Model Deployment & MLOps.
Statistical Analysis.
Cloud AI Platforms.
Explainable AI (XAI).
Business Problem Solving
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
VAM Systems is currently looking for AI / Machine Learning Engineer (On-Site) 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.
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.
Machine Learning / Deep Learning.
Statistics and Finance knowledge.
Data Preprocessing & Feature Engineering.
Model Deployment & MLOps.
Statistical Analysis.
Cloud AI Platforms.
Explainable AI (XAI).
Business Problem Solving
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
Company Industry
- Engineering Design & Consulting
Department / Functional Area
- Engineering
Keywords
- AI / Machine Learning Engineer
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