As a Senior AI Engineer, your role will involve leading the design, evaluation, and deployment of scalable machine learning and AI solutions. You will be responsible for building advanced NLP and Large Language Model (LLM) driven applications while ensuring strong engineering practices, rigorous evaluation standards, and high production reliability. Your duties will include translating business requirements into scalable AI system architectures, developing machine learning and NLP models, designing evaluation methodologies, building automated ML workflows, and providing technical leadership to a team of AI engineers and data professionals. **Key Responsibilities:**
Translate business requirements into scalable and production-ready AI system architectures. - Define modelling strategies, validation frameworks, and deployment approaches. - Design end-to-end ML ecosystems, including data ingestion, training pipelines, inference services, and monitoring systems. - Develop, fine-tune, and optimize machine learning and NLP models using Python. - Work extensively with frameworks such as PyTorch, TensorFlow, and Scikit-learn. - Build and integrate LLM-based solutions, including prompt optimization and retrieval-augmented systems. - Develop production-grade APIs and backend services using FastAPI, Flask, or Django. - Design and implement structured evaluation methodologies for NLP and ML systems. - Apply advanced performance metrics such as BLEU, ROUGE, perplexity, ranking metrics, and domain-specific benchmarks. - Build validation pipelines for bias detection, hallucination mitigation, and system robustness. - Run controlled experiments and A/B tests prior to large-scale production rollout. - Build automated ML workflows supported by CI/CD pipelines. - Containerize applications using Docker and manage source control with Git. - Deploy and manage AI systems on cloud platforms such as AWS, GCP, or Azure. - Monitor model performance, implement drift detection, and define retraining strategies. - Own AI initiatives from ideation through deployment and ongoing monitoring. - Mentor and guide AI engineers and data scientists. - Collaborate closely with product, engineering, and business stakeholders. **Qualifications Required:**
Minimum 5 years of hands-on experience delivering production-grade AI/ML solutions. - Strong proficiency in Python and the associated data science ecosystem. - Proven expertise in NLP techniques, embeddings, and LLM integration. - Practical experience with prompt engineering, fine-tuning approaches, or retrieval-augmented systems. - Solid understanding of CI/CD practices, containerization, and cloud-based deployments. - Working knowledge of SQL and NoSQL databases and core system architecture principles. As a Senior AI Engineer, your role will involve leading the design, evaluation, and deployment of scalable machine learning and AI solutions. You will be responsible for building advanced NLP and Large Language Model (LLM) driven applications while ensuring strong engineering practices, rigorous evaluation standards, and high production reliability. Your duties will include translating business requirements into scalable AI system architectures, developing machine learning and NLP models, designing evaluation methodologies, building automated ML workflows, and providing technical leadership to a team of AI engineers and data professionals. **Key Responsibilities:**
Translate business requirements into scalable and production-ready AI system architectures. - Define modelling strategies, validation frameworks, and deployment approaches. - Design end-to-end ML ecosystems, including data ingestion, training pipelines, inference services, and monitoring systems. - Develop, fine-tune, and optimize machine learning and NLP models using Python. - Work extensively with frameworks such as PyTorch, TensorFlow, and Scikit-learn. - Build and integrate LLM-based solutions, including prompt optimization and retrieval-augmented systems. - Develop production-grade APIs and backend services using FastAPI, Flask, or Django. - Design and implement structured evaluation methodologies for NLP and ML systems. - Apply advanced performance metrics such as BLEU, ROUGE, perplexity, ranking metrics, and domain-specific benchmarks. - Build validation pipelines for bias detection, hallucination mitigation, and system robustness. - Run controlled experiments and A/B tests prior to large-scale production rollout. - Build automated ML workflows supported by CI/CD pipelines. - Containerize applications using Docker and manage source control with Git. - Deploy and manage AI systems on cloud platforms such as AWS, GCP, or Azure. - Monitor model performance, implement drift detection, and define retraining strategies. - Own AI initiatives from ideation through deployment and ongoing monitoring. - Mentor and guide AI engineers and data scientists. - Collaborate closely with product, engineering, and business stakeholders. **Q
