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Lead AI Engineer

Lead AI Engineer

Ariston Services Group
Fresher
  • Posted 2 days ago
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Job Description

AI Architect

Location: Singapore

Role Overview

Job Summary:

We are seeking a visionary and technically skilled AI Architect to lead the design, development, and deployment of AI solutions across the bank in FS (Financial Services) vertical. This role will be instrumental in shaping our AI strategy, ensuring scalable and secure integration of AI technologies into core banking operations, customer experience, risk management and compliance.

Key Responsibilities:

Define and implement the bank's AI architecture and roadmap aligned with business goals.

Lead end-to-end solution design for GenAI/LLM, AI/ML models, including data pipelines, model training/finetuning, with Hand-on with LLMOps.

Collaborate with cross-functional teams (data science, engineering, business units) to identify GenAI/AI/automation use cases and deliver impactful solutions.

Ensure AI systems comply with regulatory requirements, ethical standards and data governance policies.

Evaluate and integrate emerging AI technologies, platforms, and tools.

Provide technical leadership and mentorship to AI engineers and data scientists.

Qualifications:

Bachelor's or Master's degree in Computer Science, Data Science, AI or related field.

Proven experience in AI/ML solution architecture, preferably in financial services.

Strong knowledge of cloud platforms (Azure, Databricks), Statistics & data engineering

Familiarity with regulatory frameworks and ethical AI practices in banking.

Familiarity with GenAI Observability, Traceability and Explainability using OpenAI and Anthropic models.

Excellent communication and stakeholder management skills.

Preferred Skills:

Knowledge of financial products, risk analytics and customer behavior modeling.

Certifications in AI/ML or cloud architecture are a plus.

More Info

Job Type:
Industry:
Employment Type:

Key Skills

GenAI

data pipelines

model training

LLMOps

GenAI Observability

cloud platforms

Explainability

ML models

AI architecture

finetuning

ethical AI practices

regulatory frameworks

Anthropic models

OpenAI

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