Senior AI Engineer

We are seeking a highly skilled AI Engineer to design, build, and deploy production-grade AI and Generative AI solutions that power core retail capabilities across merchandising, supply chain, pricing, stores, and digital commerce. This role is deeply hands-on and focused on turning AI models into reliable, scalable, and governed products using modern data and AI platforms Key responsibilities AI System Design and Engineering

• Design and implement AI applications for retail scenarios such as product search, recommendations, personalization, content understanding, and customer/associate assistants (chat/voice).
• Translate high-level product requirements into technical designs, model choices, and service interfaces that can be implemented and iterated quickly.
• Implement LLM- and NLP-based solutions (e.g., retrieval-augmented generation, document Q&A, summarization, classification) for internal knowledge assistants and customer-facing experiences.
• Work with front-end and back-end engineers to embed AI capabilities into web, app, store, and internal tools, focusing on usability and performance.

Data, modeling, and evaluation

• Partner with data engineers to access and prepare data from POS, ecommerce, CRM/loyalty, product catalogs, and supply-chain systems; design data contracts and schemas that support AI applications.
• Build and iterate on models using appropriate techniques (traditional ML, deep learning, embeddings, LLMs), balancing accuracy, robustness, and cost.
• Set up evaluation pipelines, offline metrics, and online experiments (A/B tests) to measure impact on key KPIs like conversion, engagement, attach rate, in-stock rate, and associate productivity.
• Instrument applications with logging, tracing, and dashboards to monitor quality, latency, and errors in production.

Engineering excellence

• Develop reliable, well-tested APIs, microservices, and jobs that run in cloud environments (AWS/Azure/GCP), using modern DevOps/CI/CD practices.
• Own the full lifecycle of features you build from design and implementation through deployment, monitoring, and iteration.
• Contribute to shared libraries, templates, and best practices that make it easier for other teams to build AI features consistently.
• Collaborate across time zones, maintaining clear communication and documentation for stakeholders.

Skill: Python, Gen AI, Azure AI, Databricks, Retail Experience Required: 12+ AI Engineer with strong expertise in Python, LLMs, Generative AI, NLP, Databricks, Azure/AWS/GCP, PyTorch/TensorFlow, REST APIs, and SQL/NoSQL. The role involves designing, developing, and deploying scalable AI/GenAI solutions for retail and eCommerce use cases such as search, recommendations, personalization, and AI assistants. Responsible for building and operationalizing ML/LLM models, implementing RAG-based solutions, integrating AI services into enterprise applications, and driving end-to-end delivery through CI/CD and cloud platforms. Experience with MLOps, model monitoring, and cross-functional collaboration is preferred., * Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related technical field.

• 3-5 years of experience building and deploying AI/ML-powered applications in production, ideally in consumer, ecommerce, or retail environments.
• Strong programming skills in Python and experience with modern ML/AI frameworks (PyTorch, TensorFlow, scikit-learn, Hugging Face, or similar).
• Experience with LLMs and NLP (prompt design, embeddings, vector databases, RAG) or strong willingness and demonstrable ability to ramp quickly.
• Hands-on experience with cloud platforms (AWS, GCP, or Azure), ML services (SageMaker, Vertex AI, Azure ML) and Databricks.
• Experience building and consuming RESTful APIs, working with SQL/NoSQL databases, and deploying services in cloud environments.
• Solid understanding of software engineering fundamentals: data structures, algorithms, testing, code reviews, and CI/CD.
• Strong communication skills and ability to collaborate with cross-functional partners across time zones.

Nice to have

• Experience in retail, ecommerce, logistics, or similar high-volume B2C contexts.
• Exposure to MLOps tools and concepts (feature stores, model registries, monitoring) and modern data stacks (Spark/Kafka/Airflow/dbt).

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