AI Engineer

AI Engineer

Job Summary

This position may be based in India or China. Applicants will see a list of available countries during the application process and should select only the country where the position is actually located.

The AI Engineer will be responsible for developing, deploying and maintaining machine learning, generative AI and agent-based solutions that address practical business needs. The role involves working with data scientists, software engineers and product teams to move AI solutions from initial concepts and prototypes into reliable production systems.

Key Responsibilities

• Develop, deploy and maintain machine learning models and AI-powered applications in production environments.

• Build scalable data pipelines that support model training, testing, evaluation and inference.

• Fine-tune and improve large language models and deep learning systems with focus on accuracy, performance, cost and response time.

• Develop Retrieval-Augmented Generation solutions covering document processing, chunking, embeddings, retrieval and re-ranking.

• Implement and manage vector databases and similarity-search systems for large-scale embedding storage and retrieval.

• Build agentic AI solutions involving tool usage, multi-step reasoning and multi-agent workflows.

• Develop reliable, long-running AI workflows using orchestration platforms such as Temporal, Airflow or equivalent technologies.

• Integrate AI and machine learning models into business applications through APIs, microservices and other software architectures.

• Apply MLOps and LLMOps practices covering deployment pipelines, model and prompt versioning, evaluation, monitoring and retraining.

• Develop prompt engineering approaches, few-shot examples and structured function-calling or output formats.

• Work with product, engineering and data teams to convert business requirements into practical technical solutions.

• Conduct experiments, A/B testing and model evaluations to assess factors such as accuracy, relevance, groundedness and hallucination rates.

• Apply responsible AI principles, including security, privacy, explainability, bias reduction and appropriate safeguards.

• Keep up with developments in artificial intelligence, machine learning and generative AI technologies.

• Document system architectures, workflows, technical decisions and implementation processes.

Strategy and Business

• Contribute to the AI team's delivery of solutions for identified business use cases.

• Understand business requirements and translate them into suitable technical solutions.

• Ensure agreed deliverables are completed within the required timelines.

Processes

• Support AI solution planning, development and delivery.

• Follow DevSecOps and software release management practices.

• Ensure appropriate risk, compliance and governance requirements are addressed.

People and Talent

• Collaborate with cross-functional teams and contribute to technical leadership where required.

• Build effective relationships with stakeholders and technology vendors.

• Support knowledge sharing, coaching and development of team members.

Risk Management

• Take ownership of delivery-related risks and communicate potential issues to relevant stakeholders promptly.

• Develop appropriate mitigation measures and risk management plans.

• Monitor identified risks throughout the delivery lifecycle.

Governance and Compliance

• Maintain awareness of the regulatory environment and requirements relevant to the organization and role.

• Follow the organization's values, Code of Conduct and applicable regulatory requirements.

• Promote high standards of ethical and responsible business conduct.

• Identify, escalate, mitigate and resolve risk, compliance and conduct issues in collaboration with relevant teams.

• Ensure AI solutions meet applicable security, privacy, audit and regulatory expectations.

Key Stakeholders

• Business stakeholders

• AI/ML engineering teams

• AI/ML product teams

• Product enablement teams

• Infrastructure teams

• Programme and project teams

Skills and Experience

• Strong understanding of software development lifecycles, including Agile, DevSecOps and CI/CD.

• Experience deploying AI/ML solutions and implementing MLOps processes.

• Knowledge of cloud and platform technologies, including AWS, Azure, containers and Kubernetes.

• Experience with APIs, microservices, data pipelines and integration architecture.

• Understanding of governance, risk management, audit and regulatory controls.

• Strong stakeholder management and communication skills.

• Ability to work effectively with technical and non-technical stakeholders.

Qualifications and Requirements

• Bachelor's or Master's degree in Computer Science, Engineering or a related discipline. Specialization in AI or Machine Learning is preferred.

• At least five years of experience in software delivery, including demonstrated involvement in complex AI/ML or data-driven solutions.

• Strong Python programming skills and experience with relevant AI/ML frameworks.

• Practical experience working with LLMs, RAG architectures, embeddings and vector search.

• Experience developing agentic AI workflows involving tools, planning, reasoning and multi-agent coordination.

• Experience using workflow orchestration technologies such as Temporal, Airflow, Step Functions or similar platforms.

• Knowledge of vector databases and semantic search technologies.

• Strong understanding of algorithms, data structures and software engineering principles.

• Experience with AWS, Azure or GCP and container technologies such as Docker and Kubernetes.

• Knowledge of SQL and data engineering tools such as Spark, Airflow or similar platforms.

• Experience with AI orchestration frameworks including LangChain and LangGraph.

• Experience using durable execution frameworks such as Temporal for reliable and fault-tolerant AI workflows.

• Familiarity with MLOps and LLMOps platforms such as MLflow, Kubeflow, SageMaker, Databricks, LangSmith or Weights & Biases.

• Strong understanding of end-to-end software delivery, governance and cross-functional engineering management.

• Exposure to modern AI/ML ecosystems, cloud platforms and contemporary engineering practices.

• Demonstrated leadership and stakeholder management experience, preferably within regulated enterprise environments.

• Strong ability to communicate complex technical concepts clearly to non-technical audiences.

• Ability to work independently while collaborating effectively with multidisciplinary teams.

• Curiosity, adaptability and commitment to continuous learning in the rapidly developing AI and generative AI field.

APPLICATION

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