Senior AI Engineer (40001866)

KEY ACCOUNTABILITIES (maximum 3 sections and 2000 words for each section)
Key Accountabilities (1)
AI Craft – Specialization
- Apply and evaluate established and emerging algorithms under production constraints; resolve complex modeling issues within project scope (e.g. data quality gaps, moderate distribution shift, performance trade-offs), while escalating highly ambiguous or cross-system problems.
- Implement and operate multi-agent workflows and stateful pipelines for defined use cases; experiment with models/frameworks hands-on and extend existing agent patterns and prompt libraries where applicable.
- Apply optimisation techniques (latency, cost, quality) using existing approaches; monitor system performance and contribute to tuning and capacity decisions within the team.
- Demonstrate solid domain expertise and continue developing advanced skills; support team members through technical execution and knowledge sharing; participate in reviews and follow established engineering practices
AI Craft – Evaluation
- Design and execute evaluation setups for features/systems using predefined approaches (holdout sets, offline metrics, structured human or LLM-based review loops).
- Analyse results, identify issues, and propose improvements to models or prompts.
- Support controlled experiments (e.g. basic A/B tests, shadow testing) under guidance; ensure adherence to evaluation standards and processes.
Key Accountabilities (2)
Engineering for Production
- Apply and contribute to testing strategies for AI systems (data validation, model regression, prompt-output checks, basic agent testing), leveraging existing tools and frameworks.
- Implement and maintain components of data platforms supporting ML and LLM/RAG workloads; consider cost, latency, and freshness trade-offs within assigned scope.
- Design and build end-to-end AI solutions for specific use cases, including data flow, serving, caching, fallback, and monitoring; ensure compatibility with existing systems and APIs following defined standards.
- Operate within established CI/CD and operational processes (deployment, drift monitoring, incident handling); participate in incident response and continuous improvement activities.
- Assist in refining internal tools, guidelines, and processes to improve team efficiency and engineering quality."
Key Accountabilities (3)
"Research & Emerging Technologies & AI Safety, Ethics & Responsible AI
- Apply and evaluate emerging AI technologies and approaches in designing and implementing solutions for specific projects/use cases.
- Apply responsible AI standards and ensure compliance with safety, ethical, and regulatory guidelines.
- Perform basic risk analysis within project scope; propose and implement mitigation measures.
- Conduct system testing and evaluation (e.g., stability, reliability); support risk assessment activities.
- Provide support in mentoring other engineers on best practices.

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
- 5+ 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