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