Job Description

Job Information

Date Opened

05/21/2025

Job Opening Status

In-progress

Job Type

Full time

Industry

Technology

City

Abu Dhabi

State/Province

Abu Dhabi

Country

United Arab Emirates

Zip/Postal Code

51133

About Us

Raqmiyat is a UAE-based IT and digital transformation company specializing in consulting, staffing, and enterprise technology solutions. We empower banking, government, and enterprise clients across the Middle East to achieve their digital objectives.

onsite resource

specialized in

Artificial Intelligence (AI), Generative AI, and Data Science

.

THE BIDDER

shall submit

6 month

and

a one-year

options for commercial proposals, outlining clear timelines, costs, and resource commitments.

18.2. Strategic AI Integration

The

AI Consultant

must have

deep expertise

in machine learning, natural language processing (NLP), predictive analytics, and generative AI. They will:



Provide a

clear strategic vision

for AI integration into CLIENT's business, operations, and strategic goals. Collaborate with CLIENT teams to

translate

business requirements into technical specifications for AI models and systems. Align solutions with

CLIENT's objectives

for scalability, security, and ethical compliance.

18.3. Requirements Gathering & Analysis

Conduct in-depth interviews

with domain experts, data owners, and stakeholders to capture business requirements, success criteria, and use cases.

Identify key performance indicators (KPIs)

and acceptance criteria for proposed AI solutions.

Review existing data infrastructure

(tools, analytics platforms) and assess data quality, availability, and security protocols. Evaluate

current AI or ML initiatives

to identify effectiveness and gaps.

18.4. AI Architecture & Roadmap

Develop an end-to-end AI architecture

tailored for CLIENT, covering data ingestion, storage, processing pipelines, model deployment, and scalability. Propose a

comprehensive AI roadmap

that aligns with CLIENT's strategic objectives, including clear milestones, resources, and timelines.

Ensure security and compliance

with industry best practices and ethical standards.

18.5. Data Governance & Strategy

Propose

data governance policies

and standards for responsible data use and compliance (e.g., GDPR, CCPA). Define

data collection, cleaning, and transformation

pipelines. Outline strategies for

data privacy

, security, and regulatory adherence. Evaluate and recommend

tools/platforms

for data management and AI workflows, ensuring alignment with CLIENT's infrastructure.

18.6. Model Development & Fine-Tuning

Select appropriate AI frameworks or open-source language models

(e.g., Llama-based, BERT-based) or domain-specific solutions.

Build initial prototypes

or Proofs of Concept (PoCs) to validate the feasibility and effectiveness of proposed AI solutions.

Fine-tune Large Language Models (LLMs)

or other ML models using CLIENT's data, optimizing hyperparameters for performance metrics (accuracy, precision, recall). Incorporate

ethical AI

principles--fairness, accountability, transparency--into model design and training.

18.7. Integration & Deployment

Collaborate with CLIENT's IT and backend development teams

to ensure seamless AI solution integration into existing systems. Define

Continuous Integration/Continuous Delivery (CI/CD) pipelines

for automated model updates, versioning, and rollback procedures.

Ensure robust APIs

and interfaces for data exchange and user interaction. Plan the

integration of pre-trained models

where beneficial, while aligning with organizational objectives.

18.8. Monitoring, Testing & Validation

Develop test cases and benchmarks

to assess model performance (accuracy, precision, recall, latency, etc.).

Conduct user acceptance testing (UAT)

with domain experts to gather feedback and refine models iteratively. Establish

monitoring metrics

(e.g., performance, resource usage) and

real-time alerting systems

for anomalies or performance degradation. Diagnose

model failures

or inaccuracies promptly and propose effective solutions for improvement.

18.9. Ethical & Regulatory Compliance

Ensure AI solutions comply

with relevant laws and regulations (GDPR, CCPA) and any CLIENT-specific standards. Maintain

audit trails, logging, and reporting

mechanisms to uphold fairness, transparency, and accountability in AI usage.

Implement an AI ethics policy

covering data privacy, model bias, transparency, and responsible AI practices.

18.10. Training & Knowledge Transfer

Provide training materials and sessions

for stakeholders, technical teams, and end-users to ensure smooth adoption of AI solutions.

Explain complex AI concepts

to non-technical stakeholders, ensuring buy-in and successful integration into business processes. Conduct

workshops

to detail system functionalities, best practices, and ongoing maintenance needs.

18.11. Final Handover & Documentation

Document standard procedures

for data handling, model retraining, and incident response (model drift, bias, misuse). Provide

comprehensive system documentation

, including architecture diagrams, data flow maps, model specifications, and operational guidelines.

Handover all code repositories

, configurations, and credentials with clear instructions for future updates or rollbacks. Deliver a

long-term AI strategy

to guide CLIENT's continued AI maturity beyond the contract period.

Key Skills:

Advanced Technical Skills

: Proficiency in AI technologies, including machine learning, deep learning, NLP, and generative models.

Programming Languages

: Python (essential), plus familiarity with R, Java, or Scala.

Data Science Skills

: Strong background in statistical analysis, data preprocessing, data visualization, and predictive modeling.

Business Acumen

: Ability to align AI initiatives with CLIENT's strategic and operational goals.

Communication

: Excellent oral and written skills to convey AI concepts to both technical and non-technical teams.

Problem-Solving

: Analytical mindset with the ability to design effective AI-driven solutions.

Ethical Practices

: Understanding of ethical considerations in AI, including bias mitigation and regulatory compliance.

Continuous Learning

: Demonstrated commitment to staying updated with evolving AI trends and best practices.

Key Qualifications:

Education

: Bachelor's, Master's, or PhD in Computer Science, Artificial Intelligence, Data Science, or a related field.

Certifications

: AI-related certifications (e.g., from major cloud providers or recognized institutions) are preferred.

Work Experience

: Minimum

8 years

of experience in AI or Data Science roles (government sector experience is a plus).

3 case studies

demonstrating end-to-end AI project delivery from inception to completion.

4.

Experience with Large Datasets & Data Security

Proven ability

to handle and advise on approaches for large or complex datasets, ensuring

data privacy and security

. Familiarity with

on-premises solutions and plus points on cloud-based

solutions (AWS, Azure, GCP) for scalable AI deployments.

5.

Ethical, Regulatory, and Compliance Requirements

Commitment to ethical AI

practices, including fairness, transparency, and accountability. Compliance with

CLIENT's internal policies

and relevant data protection regulations (GDPR, CCPA, etc.).

Deliverables

Requirements Document & AI Roadmap

Data Governance & Strategy Document

AI Architecture Blueprint

Model Prototypes & Proofs of Concept (PoCs)

Fine-Tuned Models

& deployment guidelines

Monitoring & Maintenance

plans (metrics, alerts, CI/CD)

Ethical & Compliance Framework

(audit logs, fairness principles)

Training Materials & Sessions

Final Handover Documentation

(all code, credentials, SOPs)

Additional Expectations

Ensure

clear and consistent communication

with CLIENT stakeholders and management. Adhere to

CLIENT's branding, security, and operational

guidelines. Incorporate

iterative feedback

for continuous improvement of AI solutions. * Provide

regular project status updates

and maintain transparency regarding progress, risks, and challenges.