Key Responsibilities
• Contribute to responsible AI practices, model evaluation, and observability/monitoring for the systems you own.
• Work closely with engineering, product, and data science teams to translate business requirements into working AI features.
• Guide and review the work of junior AI engineers; set coding, testing, and deployment standards within the team.
• Track developments in foundation models, multi-modal AI, and emerging GenAI techniques, and apply them to product improvements.
• Take proof-of-concept (PoC) projects through to production-grade deployments with a focus on latency, cost, and reliability.
• Ensure AI systems comply with data privacy regulations (GDPR, DPDP) and internal security policies.
Required Qualifications
• 5+ years of overall software/ML engineering experience, with at least 2 years hands-on in Generative AI and Machine Learning in a production environment.
• Strong proficiency in Python and ML frameworks: PyTorch, TensorFlow, Hugging Face Transformers.
• Proven experience building and deploying LLM-based applications (prompt engineering, fine-tuning, RLHF).
• Strong working knowledge of RAG architectures, embedding models, and vector databases.
• Experience with cloud platforms (AWS, Azure, or GCP) and containerisation (Docker, Kubernetes).
• Solid understanding of MLOps practices: CI/CD for ML, model versioning, monitoring, and drift detection.
• Hands-on experience with orchestration frameworks such as LangChain, LlamaIndex, or AutoGen.
• Good system design skills with the ability to build scalable, fault-tolerant AI services.
• Strong communication skills, with the ability to explain technical trade-offs to non-technical stakeholders.
• Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field (or equivalent practical experience).
