Sr. AI Engineer
target
Job Description
- Build production-grade AI/ML applications, services, and platforms using Python and modern engineering practices, with a focus on clean code, testing, documentation, reliability, scalability, and maintainability.
- Design and develop scalable data and ML pipelines for batch, streaming, and near-real-time processing using distributed data frameworks, Kafka or event-driven architecture, workflow orchestration tools, and enterprise data platforms.
- Implement end-to-end model training, evaluation, deployment, inference, monitoring, and lifecycle management workflows that can scale across large datasets and high-impact enterprise use cases.
- Partner with Data Scientists to convert prototypes, notebooks, statistical models, ML models, GenAI workflows, and optimization algorithms into reliable, reusable, and production-ready systems.
- Build and deploy REST APIs, microservices, model-serving endpoints, batch scoring jobs, and event-driven integrations that expose AI/ML capabilities to downstream applications and business workflows.
- Design scalable inference systems for promotion decisioning, segmentation, redemption prediction, offer ranking, campaign simulation, and personalized marketing use cases.
- Work with SQL, NoSQL, object stores, feature stores, and distributed data systems to store, retrieve, transform, and manage structured and unstructured data for AI/ML applications.
- Support production deployment and release management through CI/CD, containerization, automated testing, model versioning, automated validation, release controls, rollback strategies, and environment management.
- Implement MLOps capabilities including feature pipelines, model registries, experiment tracking, automated retraining, performance monitoring, data drift detection, model drift detection, lineage, governance, and reproducibility.
- Implement observability and reliability mechanisms, including logging, metrics, traces, dashboards, alerting, error handling, incident response, and root-cause analysis for production AI systems.
- Optimize AI/ML services for latency, throughput, cost, scalability, reliability, and operational performance.
- Evaluate and integrate Generative AI and LLM components, including prompt workflows, RAG pipelines, embeddings, vector databases, model evaluation, guardrails, safety controls, and orchestration patterns where applicable.
- Explore agentic AI workflows, including planning, tool use, multi-step reasoning, workflow orchestration, and human-in-the-loop patterns for internal productivity and decision-support use cases.
- Contribute to design reviews, architecture discussions, code reviews, operational readiness reviews, and engineering standards for AI/ML systems.
- Troubleshoot production issues across data pipelines, model services, APIs, optimization workflows, and downstream integrations; identify root causes and implement durable fixes.
- Create reusable frameworks, libraries, templates, and best practices that improve AI engineering velocity and quality across the team.
- Communicate technical designs, trade-offs, system behavior, risks, and production performance clearly to technical and non-technical stakeholders.