SENIOR DATA SCIENTIST - Python

Role Summary The Senior Associate AI/ML Engineer designs, builds, and deploys production-grade machine learning and multimodal AI solutions that operate across text, image, audio, and video data. The role focuses on transforming unstructured and semi-structured data into scalable AI services that power search, recommendations, automation, analytics, and content intelligence use cases. This engineer owns model development, pipeline implementation, optimization, and deployment, while contributing to MLOps practices and mentoring junior team members. Key Responsibilities 1. Model & Pipeline Development ? Build and deploy multimodal ML models across: o Natural Language Processing (NLP) o Computer Vision (CV) o OCR and document understanding ? Develop robust pipelines for: o Text processing, entity extraction, and classification o Image tagging, moderation, and visual understanding o Speech-to-text and speaker-level analysis ? Implement Retrieval-Augmented Generation (RAG) pipelines with text and multimodal indexing. 2. Optimization & Performance Engineering ? Optimize model inference for latency, throughput, and cost efficiency across batch and near real-time workloads. ? Apply optimization techniques including: o Batching and asynchronous inference o Quantization, pruning, or distillation o GPU and accelerator utilization tuning ? Analyze and troubleshoot model performance in production environments. 3. MLOps, LLMOps & Deployment ? Build and maintain CI/CD pipelines for ML workloads using: o GitHub Actions, Azure DevOps, or Jenkins ? Deploy models as cloud-native microservices, leveraging: o Docker, Kubernetes (AKS) and FastAPI ? Use Azure Machine Learning for: o Experiment tracking o Model registry o Training pipelines and deployment ? Implement monitoring and observability for models and pipelines: o Metrics, logging, alerts, and drift detection (e.g., Prometheus, Grafana) 4. Application & Platform Integration ? Integrate AI capabilities into enterprise applications such as: o Search and recommendation systems o Knowledge, document, or content platforms o Auto-tagging, summarization, transcription, and moderation workflows ? Design and expose inference and retrieval APIs for downstream consumption. ? Collaborate with backend, data, and platform teams to ensure scalable and secure AI integrations. 5. Collaboration & Mentorship ? Partner with product managers, data scientists, and engineers to translate business requirements into deployable AI solutions. ? Review code, promote best practices, and mentor junior engineers. ? Contribute to reusable components, documentation, and engineering standards. ? Required Skills & Expertise Core Technical Skills ? Strong proficiency in Python with PyTorch and/or TensorFlow. ? Hands-on experience with: o NLP, computer vision or speech models ? Working knowledge of LLM and orchestration frameworks: o LangChain, LlamaIndex or equivalent ? Experience with vector search and semantic retrieval: o FAISS, Pinecone, Weaviate, or Azure AI Search ? Solid understanding of Docker, Kubernetes, and CI/CD pipelines. Preferred Skills ? Experience with Azure AI and ML ecosystem, including: ? Azure OpenAI ? Azure Data Lake or related data services ? Familiarity with real-time inference, streaming data, or distributed ML systems. Qualifications ? 3?5+ years of hands-on experience in ML engineering or applied AI roles. ? Bachelor?s degree in Computer Science, AI, Engineering, or related field. Role Summary The Senior Associate AI/ML Engineer designs, builds, and deploys production-grade machine learning and multimodal AI solutions that operate across text, image, audio, and video data. The role focuses on transforming unstructured and semi-structured data into scalable AI services that power search, recommendations, automation, analytics, and content intelligence use cases. This engineer owns model development, pipeline implementation, optimization, and deployment, while contributing to MLOps practices and mentoring junior team members. Key Responsibilities 1. Model & Pipeline Development ? Build and deploy multimodal ML models across: o Natural Language Processing (NLP) o Computer Vision (CV) o OCR and document understanding ? Develop robust pipelines for: o Text processing, entity extraction, and classification o Image tagging, moderation, and visual understanding o Speech-to-text and speaker-level analysis ? Implement Retrieval-Augmented Generation (RAG) pipelines with text and multimodal indexing. 2. Optimization & Performance Engineering ? Optimize model inference for latency, throughput, and cost efficiency across batch and near real-time workloads. ? Apply optimization techniques including: o Batching and asynchronous inference o Quantization, pruning, or distillation o GPU and accelerator utilization tuning ? Analyze and troubleshoot model performance in production environments. 3. MLOps, LLMOps & Deployment ? Build and maintain CI/CD pipelines for ML workloads using: o GitHub Actions, Azure DevOps, or Jenkins ? Deploy models as cloud-native microservices, leveraging: o Docker, Kubernetes (AKS) and FastAPI ? Use Azure Machine Learning for: o Experiment tracking o Model registry o Training pipelines and deployment ? Implement monitoring and observability for models and pipelines: o Metrics, logging, alerts, and drift detection (e.g., Prometheus, Grafana) 4. Application & Platform Integration ? Integrate AI capabilities into enterprise applications such as: o Search and recommendation systems o Knowledge, document, or content platforms o Auto-tagging, summarization, transcription, and moderation workflows ? Design and expose inference and retrieval APIs for downstream consumption. ? Collaborate with backend, data, and platform teams to ensure scalable and secure AI integrations. 5. Collaboration & Mentorship ? Partner with product managers, data scientists, and engineers to translate business requirements into deployable AI solutions. ? Review code, promote best practices, and mentor junior engineers. ? Contribute to reusable components, documentation, and engineering standards. ? Required Skills & Expertise Core Technical Skills ? Strong proficiency in Python with PyTorch and/or TensorFlow. ? Hands-on experience with: o NLP, computer vision or speech models ? Working knowledge of LLM and orchestration frameworks: o LangChain, LlamaIndex or equivalent ? Experience with vector search and semantic retrieval: o FAISS, Pinecone, Weaviate, or Azure AI Search ? Solid understanding of Docker, Kubernetes, and CI/CD pipelines. Preferred Skills ? Experience with Azure AI and ML ecosystem, including: ? Azure OpenAI ? Azure Data Lake or related data services ? Familiarity with real-time inference, streaming data, or distributed ML systems. Qualifications ? 3?5+ years of hands-on experience in ML engineering or applied AI roles. ? Bachelor?s degree in Computer Science, AI, Engineering, or related field. TIPTLG01-9-1 Role Summary The Senior Associate AI/ML Engineer designs, builds, and deploys production-grade machine learning and multimodal AI solutions that operate across text, image, audio, and video data. The role focuses on transforming unstructured and semi-structured data into scalable AI services that po... ... Role Summary The Senior Associate AI/ML Engineer designs, builds, and deploys production-grade machine learning and multimodal AI solutions that opera... Azure openAI, Azure Data lake, Natural Language Processing (NLP), Computer Vision