A large-scale technology organisation is hiring a Lead/Staff Data Engineer to join a high-impact team at the intersection of data engineering, business analytics, ML engineering, and backend engineering. The team builds pipelines, platforms, and tools that enable Data Science and Performance Marketing teams to run experiments in production at scale. The role involves end-to-end ownership of systems that allow experiments to launch in production at pace, partnering with experienced engineers and product owners. Candidates need a Bachelor's degree in Computer Science or a related field, 6+ years of software engineering experience including 4+ years in data/ML engineering, and expert-level skills in SQL, Python3, and a JVM-based language (Java or Scala). Familiarity with machine learning concepts, statistical analysis, and modern data/ML stacks (Spark, Hadoop, S3) is required. Relocation sponsorship is provided.
Recruitis
Company ProfileRecruitment agency advertising technology and business roles for client companies, including opportunities in Bangkok.
Company Profile
Recruitment agency advertising technology and business roles for client companies, including opportunities in Bangkok.
Open Positions
Explore current opportunities at this company.
Senior individual contributor role leading the design and improvement of machine learning-powered solutions in a production environment. The position focuses on complex user-facing AI systems, driving experimentation and model improvement, and collaborating with cross-functional teams to turn ambiguous problems into scalable solutions. The role requires proven experience as a Staff Data Scientist, Applied Scientist, or ML Engineer with end-to-end ownership of ML or LLM initiatives. Candidates need strong hands-on experience with ML platforms in production, solid fundamentals in machine learning and statistics, and the ability to communicate complex technical ideas to diverse stakeholders. An advanced degree in a quantitative discipline is preferred.
The employer is recruiting a Customer Success Manager for a product company that builds enterprise software for multi-site restaurant groups. The role owns a portfolio of enterprise and mid-market restaurant accounts, focusing on renewals, expansion, and retention rather than support. Responsibilities include managing customer relationships post-onboarding, driving renewals and upsells, uncovering operational pain points, monitoring account health, running QBRs, and feeding structured customer feedback into Product and Engineering. Candidates need around 5 years in Customer Success, Account Management, or SaaS Sales, with a track record in B2B renewals and upsells, strong discovery skills, and commercial confidence.
The Technical Product Manager will own and evolve the ML and LLM Ops platforms used across the organisation. The role involves shaping how teams build, evaluate, deploy, monitor, and scale machine learning models and LLM-powered applications. This is a deeply technical position focused on reducing the friction of building ML applications to enable faster, safer, and automated development. The manager will gather requirements from ML engineers and data scientists, identify platform gaps, and partner with engineering on architecture and API design.
This role involves designing, building, and evolving a production-grade AI assistant experience. The position requires operating at the intersection of data science, applied machine learning, LLM system design, experimentation, and product delivery. The individual will translate ambiguous product problems into deployable ML/LLM systems while collaborating with product managers and engineers.
The role is a hands-on technical leadership position at the intersection of data engineering, business analytics, ML engineering, and backend engineering. The team builds pipelines and platforms to support Data Science and Performance Marketing teams in running production experiments at scale. The position requires end-to-end ownership of systems that enable rapid experiment launches and involves working with structured and unstructured data at massive scale.
Recruitis seeks Software Engineers for contract roles within high-growth tech companies. Responsibilities include developing and maintaining scalable backend applications, designing and shipping new product features, and producing clean, well-documented code. The role involves participating in code reviews, troubleshooting software issues, and contributing to software architecture and framework design. Candidates must have a Bachelor's degree in Computer Science or a related field, 3+ years of experience in JavaScript or Python, and a solid understanding of object-oriented design principles.
This role involves working across the full lifecycle of RPA development, from engaging with business users and analyzing processes to building, deploying, and maintaining automation solutions. The position requires close collaboration with internal stakeholders in a dynamic environment where automation is key to operational efficiency. The developer will engage with business users to gather requirements, analyze processes, and design RPA solutions using UiPath. They will also monitor, troubleshoot, and maintain bots, prepare documentation, and coordinate with internal and external teams.
Recruitis is a consultancy focused on AI & Tech Talent. The Recruitment Consultant (Senior-Principal) will build a new growth function, own a recruitment desk, and manage end-to-end 360 recruitment. The role involves business development, client relationship management, and delivering high-quality hiring outcomes for senior professional roles. The position requires a consultative mindset and strong commercial awareness.
This role involves designing, building, and improving large-scale machine learning systems for automated optimization and decision-making. The position requires a hands-on technical leader who combines software engineering fundamentals with machine learning expertise. Responsibilities include designing processing pipelines, developing production-grade systems, and taking end-to-end ownership of machine learning solutions from design through deployment and monitoring.