LLM Engineer

• Engineering and deploying production‑ready generative AI solutions, including LLMs, VLMs, and multimodal models, with a strong emphasis on inference, scalability, and reliability
• Designing and operating LLM Ops pipelines, including model versioning, fine‑tuning, evaluation, deployment, rollback, and lifecycle management
• Building and maintaining AI platforms and services that support prompt management, embeddings, vector search, retrieval‑augmented generation (RAG), and tool‑calling workflows
• Integrating generative AI capabilities into enterprise applications using APIs, microservices, and event‑driven architectures
• Implementing MLOps best practices, including CI/CD for models, automated testing, performance benchmarking, observability, logging, and cost monitoring
• Optimizing model performance across latency, throughput, accuracy, and cost using techniques such as quantization, catching, batching, and model routing
• Collaborating with cloud, data, security, and product teams to ensure solutions meet enterprise standards for security, governance, and responsible AI
• Producing clear technical documentation and operational runbooks and communicating delivery status and business value to stakeholders
• Mentoring engineers and contributing to reusable frameworks, standards, and platform capabilities.

Lead Generative AI Engineer

• Design, build, and maintain scalable production-grade ML pipelines for data ingestion, model training, and inference
• Implement automated workflows for data preprocessing, feature engineering, and model retraining
• Collaborate with data scientists to operationalize ML models and ensure smooth transition from experimentation to production
• Deploy and manage ML models in production environments using cloud-based services (AWS preferred)
• Implement monitoring frameworks for data drift, model drift, and performance degradation
• Maintain high availability, reliability, and scalability of deployed models through robust engineering practices
• Take end-to-end ownership of MLOps initiatives, from design through deployment and continuous monitoring
• Promote best practices in coding, data handling, and project management within the data science team, ensuring high-quality deliverables
• Collaborate with AI research teams to integrate Generative AI solutions into ML workflows and pipelines

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Lead Generative AI Engineer

Machine Learning Engineer – MLOps, AI Infrastructure