We are seeking a skilled MLOps & Agentic Platform Engineer. This role involves managing model registries, developing continuous training loops, and implementing A/B testing infrastructure. The ideal candidate will have a strong DevOps/MLOps background and be adept at deploying scalable microservices and building observability dashboards.

Responsibilities:

• Manage model registries, continuous training loops, and A/B testing infrastructure.
• Deploy agents as scalable microservices on Kubernetes.
• Build observability dashboards to track token usage, latency, and agent reasoning paths.

Qualifications:

• Strong DevOps/MLOps background (Kubernetes, Docker, Terraform).
• Experience with MLflow, Weights & Biases, or LangSmith.
• Knowledge of building scalable microservice architectures.

Capco

Capco is a leading global technology and management consultancy that excels in driving digital transformation within the financial services and insurance sectors. With an unwavering commitment to delivering practical solutions, we empower our clients to navigate the complexities of today's fast-paced market. Our integrated services span across major financial hubs worldwide, ensuring that we are always close to our clients and their needs.

At Capco, we possess not only the expertise but also the vision and passion necessary to lead transformative change. As we embark on an exciting growth journey, this is the perfect moment to join our team. We are expanding geographically, increasing our workforce, and poised to disrupt the consulting landscape across APAC with our entrepreneurial spirit and agile methodologies.

About the Role

We are thrilled to introduce an opportunity for a talented AI Engineer to join our Hong Kong team. This role is designed for a technology innovator who thrives on solving complex challenges and is passionate about building AI-driven solutions that transform the Financial Services industry. As an AI Engineer at Capco, you will play a pivotal role in shaping the future of banking and insurance by leveraging cutting-edge AI technologies to deliver intelligent, scalable, and compliant solutions. Your work will enable our clients to unlock new efficiencies, enhance customer experience, and stay ahead in a rapidly evolving digital landscape. If you are excited about applying advanced AI techniques to real-world business problems and driving measurable impact, we want you on our team.

Key Responsibilities

• Build multi-agent workflows to optimize business processes within banking and financial services.
• Develop and maintain software pipelines for data processing, model training, and deployment using Python-based frameworks.
• Train, fine-tune, and optimize neural network models, including LLMs, for specific use cases such as risk assessment and customer query resolution.
• Collaborate with data scientists, engineers, and business stakeholders to translate requirements into production-ready AI applications.
• Ensure AI models adhere to banking regulations, data privacy standards (e.g., GDPR, CCPA), and ethical AI practices.
• Conduct experiments, evaluate model performance, and iterate solutions to improve accuracy and efficiency.
• Monitor deployed solutions in production, troubleshooting issues, and implement updates as needed.
• Stay updated on emerging AI trends, particularly in NLP and LLMs, and contribute to knowledge sharing within the team.
• Participate in code reviews, agile ceremonies, and CI/CD processes.

Experience and Qualifications

• 4–10 years of professional experience in AI/ML engineering, with a focus on software development and deployment.
• Strong software engineering experience with Python and libraries such as TensorFlow, PyTorch, scikit-learn, and Huggingface.
• Demonstrated experience in NLP, including projects involving text classification, entity recognition, or similar.
• Hands-on experience training and fine-tuning neural network models, with a track record of optimizing for performance and scalability.
• Proven expertise working with LLMs (e.g., GPT, BERT) and building agentic solutions using frameworks like LangChain, LangGraph, or ADK.
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud) for model deployment and data management.
• Solid understanding of machine learning concepts, including supervised/unsupervised learning, evaluation metrics, and bias mitigation.
• Experience with version control systems (e.g., Git) and agile methodologies.
• Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.

Preferred Qualifications

• Experience in financial services domains such as fraud detection, credit scoring, or customer service automation.
• Familiarity with big data tools (e.g., Spark, Hadoop) and databases (SQL/NoSQL).
• Knowledge of graph databases and knowledge graphs.
• Understanding DevOps practices, containerization (Docker, Kubernetes), and MLOps workflows.
• Publications, open-source contributions, or certifications in AI/ML.
• Strong communication skills for presenting technical concepts to non-technical stakeholders.

ABOUT US

Capco is global technology and business consultancy with a focus on the financial services sector. We are passionate about helping our clients succeed in an ever-changing industry, combining innovative thinking with unique expert know-how.  The solutions we offer to our customers every day are as diverse as our employees.

We are/have:

• Experts across the Capital Markets, Insurance, Payments, Retail Banking and Wealth & Asset Management domains.
• Deep knowledge in various financial services offerings including Finance, Risk and Compliance, Financial Crime, Core Banking etc.
• Committed to growing our business and hiring the best talent to help us get there
• Focused on maintaining our nimble, agile and entrepreneurial culture.

THE ROLE

We are currently seeking a Data Analytics Lead to drive the strategy and growth of our industry recognised, and award-winning Data Practice. To be considered for this role you will be a respected authority in the analytics industry, with experience in helping to build and run an analytics consulting business. This is an exciting opportunity to join a growing team of multi-disciplinary data practitioners, who you will direct and collaborate with to develop new businesses and deliver cutting-edge analytics, data science and AI projects for top-tier Financial Services clients. You will play a key role in helping to direct and drive the growth of data analytics capability, identifying business development opportunities for new and existing clients, and aiding the development of client proposals, and propositions.

You will help to develop the HK Data Practice and support the broader Global Data Practice on industry changing topics such as Advanced Analytics, Artificial Intelligence, Intelligent Automation, and Machine Learning. Collaboration, enthusiasm, and encouragement are key in ensuring we maintain our culture and working in an environment where clients become colleagues. We are looking for a candidate who will empower the team, drive high standards, grow capability, and deliver customer focused outcomes.

AS DATA ANALYTICS LEAD YOU WILL

• Identify and research business development opportunities on new or existing clients and aid the development of client proposals and bids.
• Have a strong commercial focus, ability to develop client relationships, direct sales opportunities and develop data propositions.
• Build market networks and relationships, to enhance the market awareness of the HK Data Practice.
• Contribute to internally and externally facing innovation practice in your field
• Create a collaborative culture, drive talent development, provide mentoring and coaching for those developing their careers in data
• Be comfortable working across multiple projects and initiatives, driving the growth of new and existing data capabilities, engaging internal and external stakeholders.

SKILLS & EXPERTISE

• 8+ years of consulting experience, likely in a senior management or director position.
• Experience in spearheading new business opportunities.
• Understanding of common data concepts e.g., Data Strategy, Data Management, Data Governance, Data Architecture, Data Privacy, Data Literacy
• Expertise in one or more of the following Data Science, Analytics & AI concepts: Machine Learning, Business Intelligence, MLOps, Analytics operating models, AI development and operationalisation, Data & AI Ethics, Data Analysis
• Ability to develop working relationships with a wide variety of clients, from C-suite to technical resources, balancing conflicting priorities.
• Background managing business and technical teams to deliver projects of varying sizes, from incubating innovative products to delivering large scale productionised analytics and data science models.
• Essential experience working within Financial Services, Capital Markets, Insurance, or Wealth & Asset Management, in a data science, analytics and AI related role.
• Desired experience in programme execution, business analysis and change management – proven ability to shape, design, and manage the execution of data programmes.
• Experienced working in a multi-client environment that emphasises collaboration, and teamwork.
• Experienced in recruiting and developing teams.

We have been informed of several recruitment scams targeting the public. We strongly advise you to verify identities before engaging in recruitment related communication. All official Capco communication will be conducted via a Capco recruiter.

Location: Hong Kong or Remote
Type: Full Time

The Role

As a Software Engineer at Nex, you will contribute to building the technical foundations that power our platform's most demanding capabilities. You will focus on either ML Engineering or Framework Engineering, two complementary specializations that drive research velocity and product performance.

In the ML Engineering track, you will build the infrastructure that accelerates machine learning research: training pipelines, data workflows, model integration systems, and the tools that enable rapid experimentation. Your work ensures researchers can iterate reliably and move experiments toward production readiness.

In the Framework Engineering track, you will own the design and development of our cross-platform software framework for detection, sensing, haptics, and ML inference. You will build the real-time systems, hardware abstraction layers, and developer APIs that enable multimodal interaction on Nex Playground.

Either path offers the opportunity to work on deeply technical problems in machine learning systems, data infrastructure, sensing technologies, and real-time inference. You will be part of a small, highly technical team that values both specialization and collaboration, with clear ownership of core technology areas.

The Mindset

You are drawn to solving complex technical challenges at the intersection of research and production engineering. You care deeply about building systems that are reliable, performant, and maintainable. You thrive in environments where technical depth matters, where your expertise in ML systems, distributed computing, or real-time software directly shapes what the platform can do.

What You’ll Do

ML Engineering Track

• Design and build training pipelines, data workflows, and model integration systems
• Develop infrastructure that accelerates research iteration and reduces turnaround time
• Build systems for data collection, curation, and preprocessing at scale
• Create tools and automation that move experiments toward production readiness
• Optimize data pipelines for reliability, performance, and observability
• Collaborate with ML researchers to understand their needs and remove technical blockers
• Work on model serving infrastructure and integration with the production framework

Framework Engineering Track

• Design and develop a robust, performant, cross-platform framework for sensing, detection, haptics, and ML inference
• Build efficient runtime execution systems and hardware abstraction layers
• Develop developer APIs that make complex sensing and inference accessible
• Optimize for real-time performance on multimodal data streams
• Work on camera, microphone, and haptics integration
• Implement code that runs reliably across different platforms and hardware configurations
• Improve framework stability, performance, and developer experience

Both Tracks

• Write clean, well-tested code that maintains high engineering standards
• Participate in code reviews and help raise the engineering bar across the team
• Contribute to shared tools, infrastructure, and cross-role projects (20% Time)
• Work with the dual-leadership model (Engineering Manager and Tech Lead) to understand priorities and technical direction
• Document systems and decisions to support team knowledge sharing

Must Have

ML Engineering Track

• 3+ years of professional software engineering experience in building production ML systems, training infrastructure, or research platforms
• Proficiency in Python, additional experience with at least one other systems language (C++, C#, Java, Rust, or Go)
• Hands-on experience with PyTorch or TensorFlow in production or research environments
• Experience building or maintaining ML training pipelines or data workflows
• Familiarity with model deployment, inference optimization, or MLOps practices

Framework Engineering Track

• 3+ years of professional software engineering experience in building real-time software frameworks, cross-platform systems, or performance-critical applications
• Proficiency in C++/C#, additional experience with at least one other systems language (C++, C#, Java, Rust, or Go)
• Experience with cross-platform development
• Proven track record with performance optimization, real-time systems, or hardware abstraction layers
• Experience building developer APIs or software frameworks used by other engineers

Nice To Have

For ML Engineering Track

• Experience with distributed training systems or GPU-accelerated computing
• Knowledge of data versioning, experiment tracking, or ML metadata management
• Familiarity with containerization (Docker) and orchestration tools
• Contributions to open-source ML projects or research publications

For Framework Engineering Track

• Experience building real-time systems or low-latency software
• Familiarity with computer vision, signal processing, or embedded systems
• Experience with cross-platform development or hardware abstraction
• Knowledge of performance optimization and profiling
• Experience with sensing technologies (cameras, microphones, or similar)
• Familiarity with CI/CD pipelines and automated testing

For Either Track

• Experience working in small, high-performance technical teams
• Background in startups, high-growth environments, or consumer product companies
• Passion for pushing technical boundaries and deep problem-solving

Flow Traders is looking for a Senior Research Engineer to join our Hong Kong office. This is a unique opportunity to join a leading proprietary trading firm with an entrepreneurial and innovative culture at the heart of its business. We value quick-witted, creative minds and challenge them to make full use of their capacities. 

As a Senior Research Engineer, you will be responsible for helping to lead the development of our trading model research framework and using it to conduct research to develop models for trading in production. You'll expand the framework to become global standard way of training, consuming, combining, and transforming any data source in a data-driven systematic way. You will then partner with Quantitative Researchers to build the trading models themselves.

What You Will Do

• Help to lead the development and global rollout of our research framework for defining and training models through various optimization procedures (supervised learning, backtesting etc.), as well as its integration with our platform for deploying and running those models in production
• Partner with Quantitative Researchers to conduct research: test hypotheses and tune/develop data-driven systematic trading strategies and alpha signals

What You Need to Succeed

• Advanced degree (Master's or PhD) in Machine Learning, Statistics, Physics, Computer Science or similar
• 8+ years of hands-on experience MLOps, Research Engineering, or ML Research
• A strong background in mathematics and statistics
• Strong proficiency in programming languages such as Python, with experience in libraries like numpy, pytorch, polars, pandas, and ray.
• Demonstrated experience in designing and implementing end-to-end machine learning pipelines, including data preprocessing, model training, deployment, and monitoring
• Understanding of and experience with modern software development practices and tools (e.g. Agile, version control, automated testing, CI/CD, observability)
• Understanding of cloud platforms (e. g., AWS, Azure, GCP) and containerization technologies (e. g., Docker, Kubernetes)

Flow Traders does not accept unsolicited resumes from any professional staffing or search firms. All resumes, and any other information identifying potential candidates, submitted to any employee at Flow Traders via-email, the Internet or directly without a valid and signed search agreement will be deemed free to contact by Flow Traders without any restrictions and no placement fee of any kind will be paid in the event the candidate is hired by Flow Traders.

About the role 

We are seeking an experienced Machine Learning Lead to helm our Machine Learning team. 

In this pivotal role, you will be the engineering architect behind Vulcan’s core AI capabilities. You will act as the nexus between Research, Platform, and Product. Your mission is to translate cutting-edge findings on GenAI threats into robust, production-ready machine learning models that power our GenAI Security Guardrails (Blue Team) and Automated Vulnerability Assessment (Red Team). 

Crucially, you will serve as the bridge between deep tech and business strategy, articulating technical constraints (like FLOPS and latency) to leadership and clients while guiding the engineering direction. 

Key Responsibilities

1. Model Development & Optimization (Training & Fine-tuning):

• Research to Production: Collaborate with the Security Research Team to operationalize new threat detection techniques. They identify the "what" (e.g., new prompt injection patterns); you determine the "how" (model architecture, training strategy). 
• Fine-tuning & Adaptation: Lead the fine-tuning of Language Models (e.g., using LoRA/PEFT) to optimize for our supported muti-lingual languages and specific security intents.
• Multimodal Readiness: Prepare the system for Multimodal (Text + Image/Audio) capabilities. Evaluate and implement models to detect visual prompt injections and non-textual threats as the product evolves. 

2. MLOps& Data Infrastructure: 

• Enhance & Scale MLOps: Take ownership of our existing ML pipelines. Focus on optimizing and scaling CI/CD/CT workflows to improve training efficiency and deployment velocity. 
• Data Governance: Implement and enforce rigorous Data Versioning strategies (e.g., DVC) to ensure complete reproducibility of model artifacts and datasets.
• Monitoring & Reliability: Maintain rigorous monitoring for model drift and performance, ensuring high reliability in a production security environment.

3. Cross-Functional Implementation & Leadership:

• Platform Collaboration: Work closely with the Platform Engineering Team to integrate ML models into the broader product architecture. Ensure seamless interaction between model inference services and the main platform logic. 
• Team Leadership: Lead and mentor Machine Learning Engineers, fostering a culture of engineering rigor, code quality, and operational excellence.
• Resource Management: Manage GPU resources and compute budgets effectively for both training and inference workloads. 

4. Technical Strategy & Stakeholder Management:

• Translating Tech to Business: Act as the technical voice of the ML team. You must effectively explain complex ML concepts (e.g., FLOPS, quantization trade-offs, model latency vs. accuracy) to executive leadership and clients. 
• Cost-Benefit Analysis: Justify compute resource investments. Articulate the trade-off between infrastructure costs (GPU hours) and performance gains to non-technical stakeholders. 

Qualifications

• Experience: 5+ years in Machine Learning Engineering, with specific experience in leading technical projects or mentoring engineers.
• Communication & Business Acumen: Exceptional ability to distill complex technical topics (e.g., compute complexity, infrastructure costs) into clear, business-relevant insights for decision-makers.
• MLOps Proficiency: Proven experience in optimizing ML pipelines and infrastructure. Familiarity with tools like MLflow, Kubeflow, Airflow, and Data Versioning tools (DVC, etc.).
• Engineering First: Proficient in Python, Docker, and Kubernetes. You treat ML models as software artifacts that need testing and version control.
• NLP & LLM Expertise: Experience with Transformer architectures, Embeddings, and LLM fine-tuning. Familiarity with frameworks like PyTorch, Hugging Face, and vLLM.
• Language Support: Experience processing or fine-tuning models for multi-lingual environments. 

Nice to Have

• Multimodal Expertise: Experience working with Multimodal models (Image-to-Text, Text-to-Image, VLMs like CLIP, LLaVA).
• Security Awareness: Understanding of GenAI security threats (e.g., Prompt Injection).
• High-Performance Computing: Experience optimizing inference speed (quantization, distillation, vLLM) for real-time applications.
• Vector Database: Experience with Vector DBs for RAG applications. 

Why Join Us? 

• Innovative Environment: Be part of a company at the forefront of technology to provide security in GenAI, with opportunities to work on groundbreaking projects. 
• Growth Opportunities: Take your career to new heights with our career development programs and growth-focused culture. 
• Dynamic Team: Join a multi-cultural and dynamic team of dedicated professionals who inspire and support each other. 
• Compensation: Competitive salary and benefits package, commensurate with experience and performance. 

Application Process 

• If you're ready to embark on this exciting journey and contribute to shaping the future of GenAI security, please submit your resume outlining your relevant experience and motivation for applying.
• If you prefer a direct connection or have specific questions about our vision, feel free to reach out to our Co-founder, Alvin Kwock, via LinkedIn.