Senior AI Engineer

Experteer Overview In this role you will build and operate ML engineering platforms and scalable backend systems, with a focus on MLOps and Generative AI. You will work within a collaborative, client-focused environment to deploy LLM-powered solutions, integrate Azure OpenAI, and scale models to meet strict SLAs. You’ll drive end-to-end pipelines-from feature engineering to real-time inference-while maintaining cutting-edge knowledge of transformers and GenAI. This opportunity lets you shape impactful AI solutions across healthcare and consumer domains at ZS. Compensation / Benefits * Develop and maintain ML engineering platforms, backend systems, APIs, and microservices (FastAPI) * Implement MLOps: model KPI tracking, drift detection, feedback loops * Deploy ML/Deep Learning models (LLMs/GenAI) and integrate Azure OpenAI * Build and orchestrate model pipelines (feature engineering, inference, training) * Create LLM observability (Langfuse) and prompt management with versioning * Implement Celery or similar for asynchronous workflows * Ensure scalable design with asynchronous Python, DI, and layered architecture * Develop real-time and batch prediction pipelines * Apply NLP and computer vision for document understanding, OCR, and layout processing * Work with HuggingFace, LangChain, LlamaIndex; manage embeddings and vector search Tasks * Master’s or bachelor’s degree in Computer Science or a related field * 4+ years in Machine Learning including production LLM systems * Strong ML, DL, and fine-tuning knowledge (LLMs); transformer understanding * Backend API design using FastAPI or similar; async patterns; rate limiting * Experience with vector databases (Pinecone, Weaviate, Chroma) and embedding storage * Strong Python skills; async programming; type hints and Pydantic; SOLID principles * AI/ML concepts and integrating models into backend services * MLOps experience with model tracking (MLFlow), LLM observability (Langfuse) * NLP and computer vision experience (OCR, document understanding, PDF extraction) * Model serving with FastAPI; feature engineering; real-time and batch inference pipelines * ML frameworks: HuggingFace, Keras/TensorFlow/PyTorch; LangChain preferred; LlamaIndex for RAG * Cloud and DevOps familiarity (Azure preferred): Azure OpenAI, Key Vault, Blob Storage; Docker; CI/CD (Azure DevOps, GitHub Actions) * Big data processing and ETL/ELT pipelines; rate limiting and cost management for LLMs * Production incident management and on-call experience * English fluency; client-first mindset; collaborative approach Key requirements * comprehensive total rewards * hybrid work model * internal mobility and career progression * professional development programs * collaborative culture * global opportunities

systems Celery or similar for asynchronous workflows * Ensure scalable design with asynchronous Python, DI, and layered architecture * Develop real-time and batch prediction pipelines * Apply NLP and computer vision for document understanding, OCR, and layout processing * Work with HuggingFace, LangChain, LlamaIndex; manage embeddings and vector search Tasks * Master’s or bachelor’s degree in Computer Science or a related field * 4+ years in Machine Learning including production LLM systems * Strong ML, DL, and fine-tuning knowledge (LLMs); transformer understanding * Backend API design using FastAPI or similar; async patterns; rate limiting * Experience with vector databases (Pinecone, Weaviate, Chroma) and embedding storage * Strong Python skills; async programming; type hints and Pydantic; SOLID principles * AI/ML concepts and integrating models into backend services * MLOps experience with model tracking (MLFlow), LLM observability (Langfuse) * NLP and computer vision experience aa and document understanding, PDF extraction) * Model serving with FastAPI; feature engineering; real-time and batch inference pipelines * ML frameworks: HuggingFace, Keras/TensorFlow/PyTorch; LangChain preferred; LlamaIndex for RAG * Cloud and DevOps familiarity (Azure preferred): Azure OpenAI, Key Vault, Blob Storage; Docker; CI/CD (Azure DevOps, GitHub Actions) * Big data processing and ETL/ELT pipelines; rate limiting and cost management for LLMs * Production incident management and on-call experience * English fluency; client-first mindset; collaborative approach Key requirements * comprehensive total rewards * hybrid work model * internal mobility and career progression * professional development programs * collaborative culture * global opportunities