Staff Engineer - AI

Enterprise Data and Analytics thrives on strong relationships with our business and IT partners and working diligently to address their needs, which support TJX growth and operational stability. On this tightly knit and fast-paced team you will be constantly challenged to stretch and think outside the box. We are looking for someone who embraces the use of emerging technology to design, build, deploy, and govern AI solutions.

Responsibilities:

• Design, develop, test, and deploy AI solutions using Azure AI services to meet business requirements.
• Train, fine-tune, and evaluate AI models, including large language models (LLMs), ensuring they meet performance criteria and integrate seamlessly into new or existing solutions.
• Develop and integrate APIs to enable smooth interaction between AI models and other applications, facilitating efficient model serving.
• Collaborate effectively with cross-functional teams, including data scientists, software engineers, and business stakeholders, to deliver comprehensive AI solutions.
• Optimize AI and ML model performance through techniques such as hyperparameter tuning and model compression to enhance efficiency and effectiveness.
• Monitor and maintain AI systems, providing technical support and troubleshooting to ensure continuous operation and reliability.
• Create comprehensive documentation for AI solutions, including design documents, user guides, and operational procedures, to support development and maintenance.
• Stay updated with the latest advancements in AI, machine learning, and cloud technologies, demonstrating a commitment to continuous learning and improvement.
• Design, code, deploy, and support software components, working collaboratively with AI architects and engineers to build impactful systems and services.
• Lead medium to large initiatives, prioritizing and assigning tasks, providing guidance, and resolving issues to ensure successful project delivery.
• Design, develop, and deploy AI/ML and GenAI solutions in a cloud-native (Azure) environment.
• Build and operationalize end-to-end MLOps pipelines, including model training, deployment, monitoring, and versioning, using tools such as Azure ML, MLflow, Databricks, and Azure DevOps/GitHub.
• Implement RAG pipelines, prompt engineering, guardrails, and LLMOps best practices for scalable GenAI applications.
• Develop secure, scalable APIs and integrate with Azure AI Services (Document Intelligence, Search, Vision, Speech).
• Work with vector databases (FAISS, Weaviate, Pinecone, pgvector, Azure Cognitive Search) for embeddings and retrieval workflows.
• Collaborate in an Agile/Scrum environment, including requirements gathering, design, testing, and documentation using Jira and Confluence.
• Ensure AI solutions follow security best practices such as encryption, access control, and safe deployment principles.
• Apply ethical AI principles, including bias detection, responsible ML, and fairness.
• Troubleshoot issues using monitoring tools like App Insights, Azure Monitor, and Splunk.
• Stay current on the latest developments in GenAI, LLMs, NLP, and multimodal AI.

Requirements:

• Bachelor's degree in computer science, Engineering, or a related field.
• 5+ years of hands-on AI/ML development experience with production deployments.
• Strong proficiency in Python, SQL, and modern software engineering practices.
• Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, and core Azure cloud services.
• Expertise in version control (Git), CI/CD pipelines for ML, and cloud DevOps workflows.
• Practical experience with data engineering tools and scalable data processing (e. g., Databricks, PySpark).
• Strong communication, analytical thinking, and problem-solving abilities.
• Knowledge of security best practices for AI systems and responsible AI considerations.

Preferred Qualifications:

• Experience with LLM application frameworks such as LangChain, Semantic Kernel, and LlamaIndex.
• Experience with multimodal AI use cases and advanced GenAI workloads.
• Knowledge of feature stores and ML operations tools such as Feast, Tecton, Evidently, or WhyLabs.
• Familiarity with vector search implementation at scale.
• Ability to design guardrails and content safety workflows for LLM applications.
• Strong passion for continuous learning, research, and staying ahead of GenAI trends.