Senior Specialist - Data Sciences

Key Responsibilities Build Operate AIML Solutions Partner with Data Scientists to deploy operationalize monitor and scale machine learning models in production environments Design develop and deploy AIML and LLMpowered solutions including predictive models RAG applications AI copilots intelligent assistants and agentic workflows Develop feature engineering batch scoring realtime inference and modelserving pipelines Apply Machine Learning NLP Generative AI and Agentic AI techniques to automate business processes improve decisionmaking and enhance operational efficiency Build prompt engineering vector search semantic retrieval and knowledgegrounding capabilities for enterprise AI applications Support model experimentation validation retraining and continuous improvement initiatives Design Build Data Engineering Solutions Design develop and maintain scalable batch and streaming data pipelines supporting AI ML analytics and reporting use cases Build and optimize data ingestion transformation and curation workflows using modern data platforms such as Databricks and Azure Develop and maintain data products using lakehouse architectures Delta Lake medallion frameworks and enterprise data governance standards Integrate structured and unstructured data sources to support AI and machine learning applications Ensure data quality lineage observability reliability and performance of production data pipelines Develop reusable datasets feature engineering pipelines and data assets that support model training and inference Productionize AIML Models Build scalable APIs microservices and AI services for model deployment and consumption Implement MLOps and LLMOps practices including CICD automated testing model validation monitoring observability and performance optimization Manage model lifecycle versioning deployment rollback strategies and continuous improvement in production environments 
 Implement model monitoring drift detection performance tracking ing and retraining processes 
 Optimize model inference latency scalability reliability and cost efficiency 
 Integrate with Enterprise Data Cloud Platforms 
 Integrate AIML models with enterprise applications business workflows and cloudnative architectures 
 Collaborate with data engineering architecture and platform teams to build and consume enterprise data products 
 Leverage Databricks Azure and cloudnative services to deliver scalable AIML solutions 
 Support AI Foundry and enterprise AI platform initiatives by developing reusable frameworks accelerators and shared services 
 Contribute to enterprise AIML platform capabilities including feature stores model registries prompt registries and deployment frameworks 
 Ensure Responsible AI Governance 
 Align AI and ML solutions with organizational governance compliance privacy security and Responsible AI standards Implement monitoring explainability transparency auditability and risk controls across AIML solutions Support model validation bias detection governance frameworks and regulatory compliance requirements Ensure AI and ML solutions meet enterprise reliability security and operational support standards Collaborate Drive Innovation Partner with product owners architects data scientists data engineers and business stakeholders to deliver highimpact AIML solutions Contribute to AI engineering MLOps data engineering and software development best practices Develop reusable components frameworks and accelerators to improve delivery efficiency Stay current with emerging trends in AI ML GenAI Agentic AI MLOps Data Engineering and cloudnative technologies Required Qualifications Bachelors or Masters degree in Computer Science Engineering Data Science Machine Learning or a related field Minimum 5 years of experience building productiongrade AIML and data engineering solutions including at least 3 years of handson experience deploying and operating machine learning models in enterprise environments Strong programming skills in Python and SQL with experience developing enterprisegrade applications Handson experience with Machine Learning frameworks PyTorch TensorFlow Scikitlearn MLOps platforms and practices for model deployment monitoring and lifecycle management Databricks Apache Spark Delta Lake and Lakehouse architectures Generative AI and LLM platforms Azure OpenAI OpenAI Anthropic Claude Gemini RAG architectures Vector Databases Embeddings and Prompt Engineering AI orchestration frameworks such as LangChain LangGraph Semantic Kernel or CrewAI Experience designing and building scalable data pipelines ETLELT processes and data products Experience building REST APIs microservices and productiongrade AIML applications Strong knowledge of feature engineering model serving inference pipelines and production ML workloads