Senior AI Engineer (MLOps)

Responsibilities

• Drive end-to-end application development and delivery for AI/ML initiatives.
• Design, implement, and deploy scalable AI, ML, and DL models leveraging advanced techniques.
• Build, fine-tune, and operationalize large language models and agentic AI solutions for enterprise-grade contexts.
• Utilize leading cloud-based AI services (e.g., Azure OpenAI, AWS AI, or equivalents) for integrating state-of-the-art capabilities.
• Work closely with data engineering to ensure seamless integration of data pipelines and optimized model deployment processes.
• Implement MLOps/DevOps best practices for CI/CD, monitoring, and automation of model lifecycles.
• Translate complex business challenges into technical solutions through close collaboration with stakeholders and cross-functional teams.
• Continuously optimize deployed models for reliability, scalability, and production readiness.
• Stay current on advancements in AI, ML, cloud, and data infrastructure, and proactively apply new concepts to drive innovation.
• Contribute across multiple phases of the SDLC for technology project delivery, interfacing with technical teams, vendors, and business leaders as needed.
• Author clear and comprehensive technical documentation and design artefacts.
• Conduct peer code reviews, unit testing, and remediation of defects identified during quality assurance or user acceptance test stages.
• Ensure compliance with all internal project governance and external regulatory guidelines (e.g., audit, compliance, data governance).

Requirements

Education

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related discipline.

Experience & Skills

• Minimum 5 years' experience in AI/ML/DL development, with advanced knowledge in deep learning and generative AI.
• Strong proficiency in Python and common frameworks such as PyTorch or TensorFlow, and experience with distributed computing (e.g., Apache Spark) and SQL.
• Hands-on experience with at least one open-source or managed MLOps platform (such as Kubeflow or MLflow).
• Demonstrated ability to leverage enterprise cloud AI services (e.g., Azure AI or comparable platforms) for solution delivery.
• Proficiency with data engineering concepts and tooling (e.g., cloud data factories, Delta Lake or equivalent).
• Production experience deploying generative and agentic AI architectures (e.g., LangChain, semantic kernel, RAG pipelines, prompt engineering, vector databases).
• Experience working with Agile and Waterfall methodologies in dynamic environments.
• Solid understanding of DevOps and MLOps, including CI/CD automation, monitoring, version control, and familiarity with secure software development practices (DevSecOps).

EA Licence: 16S8091

EA Reg No.: R1549798