Techcombank đang tuyển dụng Expert, AI Engineering (40001865) — vị trí làm việc tại TP. Ha Noi.
Thông tin tuyển dụng Techcombank
| Thông tin | Chi tiết |
|---|---|
| Vị trí | Expert, AI Engineering (40001865) |
| Ngân hàng | Techcombank |
| Đơn vị | AI Transformation Division |
| Địa điểm | TP. Ha Noi |
Mô tả công việc
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
Cách thức ứng tuyển
Xem chi tiết và ứng tuyển tại: Techcombank
Ngày đăng: 14/09/2026
Nguồn: Techcombank
Đăng bởi: UB Job Crawler