Expert, AI Engineering (40001865)

Job Purpose

- Lead the design and delivery of advanced AI solutions, ensuring high performance, scalability, and cost efficiency in production environments. 
- Solve complex AI challenges (modeling, evaluation, multi-agent systems) and develop reusable frameworks and standards adopted across teams. 
- Ensure AI systems are developed and operated responsibly, addressing safety, ethical risks, and governance requirements. 

Key Accountabilities (1)

AI Craft – Specialization 
- Evaluate novel algorithms and training techniques against production constraints; diagnose and unblock the hardest cross-cutting Modeling problems (severe distribution shift, low-resource settings, multi-task architecture); build reusable Modeling patterns adopted across teams. 
- Implement complex multi-agent orchestration, long-running stateful workflows, and quality optimisation at scale; evaluate new models and frameworks hands-on; build reusable agent patterns and prompt libraries adopted across teams. 
- Evaluates and adopts advanced optimisation techniques; designs multi-tenant serving and capacity strategies; builds performance-regression frameworks and cost dashboards adopted across teams. 
- Recognised expert in the sub-domain inside the org; selects between competing approaches based on production constraints; reviews and unblocks others' sub-domain work; tackles the harder problems (low-resource scenarios, distribution shift, real-time constraints); the person teams escalate to. 

AI Craft – Evaluation 
- Build evaluation tooling and automated experiment pipelines adopted across teams 
- Design and run online A/B tests with statistical rigour & calibrate human and LLM-judge evaluation 
- Resolve the hardest evaluation problems (subjective tasks, rare-event detection, long-tail safety). 

Key Accountabilities (2)

Engineering for Production 
- Design testing strategies for AI systems (data validation, model regression, prompt-output, agent-trajectory tests); evaluate and introduce new tooling; build internal libraries, SDKs, and code templates that raise the engineering floor across teams. 
- Architect data platforms that serve both ML training and LLM/RAG workloads; design for cost, latency, and freshness trade-offs; build reusable data-quality, lineage, and observability tooling adopted across teams. 
- Architect complex AI systems combining ML and agentic components; design for graceful degradation, cost-efficiency, evolvability, and multi-tenancy; build reference implementations and integration patterns adopted by other teams; lead migration and convergence of inherited or siloed systems where warranted; balance near-term delivery against long-term architectural coherence. 

Key Accountabilities (3)

Research & Emerging Technologies & AI Safety, Ethics & Responsible AI 
- Conduct in-depth evaluation of emerging AI technologies within the assigned domain/project; build prototypes to validate feasibility and risks; provide recommendations for appropriate implementation approaches for specific use cases.
- Contribute to the development and refinement of responsible AI standards within the domain; ensure solutions comply with established guidelines and regulations.
- Analyse ethical and safety risks of AI systems at solution/project level; propose appropriate risk mitigation measures.
- Participate in designing and executing testing activities (e.g., adversarial testing, risk assessments) to improve system robustness and reliability.
- Support AI risk assessments in product reviews, acting as a technical advisor within the domain.

Key Relationships - Direct Manager

Senior Manager/ Director/ Head, AI Engineering 

Key Relationships - Direct Reports

Key Relationships - Internal Stakeholders

Business Tribe, Enabling Tribe (IT or Data - Engineer/Governance), division heads (Business, Finance, Risk, Corporate Affairs, IT), CEO, CIO, CDAO, Chief AI Transformation Officer, Chairman

Key Relationships - External Stakeholders

External stakeholders include vendors and partners providing professional services

Success Profile - Qualification and Experiences

Qualifications 
- Bachelor’s or master’s degree in computer science, Artificial Intelligence, Data Science, Software Engineering, Information Technology, or a related quantitative field 
- Strong foundation in mathematics, statistics, and machine learning principles 
- Relevant certifications in AI/ML, Cloud (Azure, AWS, GCP), or Data Engineering are a plus 
- English proficiency in line with Techcombank’s policy 
Work Experience 
- 8+ years of experience in AI/ML, data engineering, and software development, with proven delivery of end-to-end AI solutions in production environments 
- Strong expertise in AI specialization, including machine learning, deep learning, NLP, and LLM applications, RAG with hands-on experience in experimentation, model evaluation, and performance tuning 
- Demonstrated experience in designing and building scalable AI systems and architectures, including microservices-based solutions and distributed systems 
- Solid experience in data engineering for AI, including building ETL/ELT pipelines, data lakes/warehouses, and handling structured & unstructured data using tools like Spark, Kafka, Airflow, and SQL/NoSQL databases 
- Proficiency in programming languages such as Python, Go, Java, or Scala, and experience with AI/ML frameworks (e.g., TensorFlow, PyTorch, / JAX, transformers, fine-tuning (LoRA, DPO), distillation, evaluation.) 
- Experience in AI experimentation & evaluation, including A/B testing, model benchmarking, validation techniques, and monitoring model performance in production 
- Working knowledge of AI Safety, Ethics & Responsible AI, Red-teaming, model risk management, alignment with SBV, MAS, Basel III, ensuring fairness, transparency, explainability, and compliance with regulatory requirements 
- Experience in MLOps/LLMOps practices (Level 2), including model deployment, CI/CD pipelines, versioning, and monitoring (e.g., Kubernetes, GPU orchestration, vector DBs, CI/CD for ML, drift and bias monitoring.) 
- Proven ability in business needs analysis, translating business problems into AI-driven solutions and measurable outcomes 
- Experience managing stakeholders, collaborating with cross-functional teams (business, risk, IT), and influencing decision-making 
- Track record of delivering AI projects end-to-end using Agile methodologies, ensuring timelines, quality, and business impact 
- Exposure to emerging technologies and research trends in AI, with the ability to evaluate and adopt new approaches when relevant 
- Basic experience or potential in AI strategy development and leadership, including mentoring junior team members and contributing to capability building