Main responsibilities in this role include, but are not limited to:
Own delivery from idea to production: frame business problems, shape agentic Al solution designs, prototype quickly, and industrialize into production-ready services with measurable outcomes.
Build agentic Al applications: design and implement agents that can reason, plan, use tools, and interact with enterprise systems (e.g., advisor tooling, analytics platforms, internal APls).
Partner with stakeholders end-to-end: collaborate with Private Banking/AWM business leaders, architects, engineers, and control functions to ensure solutions deliver real value and can be adopted at scale
Engineer for governance and safety: implement evaluation strategies, guardrails, and human-in-the-loop patterns suitable for regulated banking environments (privacy, auditability, model risk expectations).
Enable reuse and scale: build reusable patterns, components, and reference implementations to accelerate delivery across a portfolio of use cases.
Operate and improve: monitor agent behavior in production, iterate on prompts/tools/logic based on evals and feedback, and continuously improve quality, cost, latency, and safety.
Who you are
Collaboration. Ownership. Passion. Courage. These are the values that guide us in being at our best - and that we imagine you share with us. This is the right role for you if you are a curious, outcome-driven and pragmatic senior scientist who can take ownership, influence stakeholders, and raise modelling and engineering standards while delivering working solutions at pace.
Your background and skills include:
Are a curious, innovative team player with a can-do attitude, always looking for ways on how we can unlock the next level for our customers.
Hands-on agenticAl experience, including designing and building agent workflows (planner-executor, tool calling, multi-agent, human-in-the-loop).
Strong LLM ops and evaluation mindset: quality/correctness, latency, cost, and safety, ability to define test sets and run disciplined experimentation.
Guardrails and Responsible Al experience: input/output validation, prompt safety, data leakage prevention, and controls appropriate for regulated environments.
Strong software engineering skills with Python (modular design, testing, version control) and comfort working in notebook-based experimentation.
Cloud and integration experience: deploying Al services in AWS, integrating via REST APIs, and familiarity with containerisation/Kubernetes and CI/CD
Data/analytics platform experience (eg., Snowflake) for grounding, retrieval, and context management. Business engineering strength: ability to translate stakeholder needs into solution requirements, KPIs, and adoption plans; st communication and collaboration skills.
Skills Required
- Hands-on experience designing and building agentic AI workflows, including planner-executor, tool calling, multi-agent, and human-in-the-loop patterns.
- Strong LLM operations and evaluation experience covering quality, correctness, latency, cost, safety, test sets, and disciplined experimentation.
- Experience implementing AI guardrails and responsible AI controls, including input/output validation, prompt safety, data leakage prevention, privacy, and auditability.
- Strong Python software engineering skills, including modular design, testing, version control, and notebook-based experimentation.
- Cloud and integration experience deploying AI services in AWS and integrating through REST APIs.
- Familiarity with containerization, Kubernetes, and CI/CD.
- Experience with data and analytics platforms such as Snowflake for grounding, retrieval, and context management.
- Ability to translate stakeholder needs into solution requirements, KPIs, and adoption plans.
- Strong communication, collaboration, stakeholder management, and business engineering skills.
Datamatics Technologies Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Datamatics Technologies and has not been reviewed or approved by Datamatics Technologies.
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Flexible Benefits — Feedback suggests flexible timings and work-from-home options are available in some roles. This flexibility is highlighted as part of the employment experience across certain postings and materials.
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Wellbeing & Lifestyle Benefits — Feedback suggests flexibility around time off and remote work supports work-life balance. These elements can help offset leaner cash components for some individuals.
Datamatics Technologies Insights
What We Do
Datamatics Technologies (DMT) was established in Dubai. We specialize in providing onsite and offshore professional services, covering the full spectrum of Data Analytics and Data Science domains. Our experience of working with diverse industry sectors such as Telecoms, Finance, Government and Manufacturing, across multiple regions enables us to engage and deliver for our clients with confidence. We can offer our full portfolio of services through resource augmentation, managed services, both on T&M or fixed price financial arrangements. Through our end-to-end managed services offering we enable our clients to cut down costs, increase profitability and focus on value addition to their core business activities. Our project and delivery management team are certified in Agile, PMI and ITIL to ensure the planning and execution are carried out using industry best practices. We are working with our clients across Middle East and Africa Region.
