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