About the job
Join our dynamic team as a Senior AI Engineer / MLOps Engineer, where you will spearhead the design, development, deployment, and optimization of robust, scalable AI and machine learning solutions. We seek an individual with a profound understanding and practical experience in AI engineering, machine learning, MLOps, cloud-native architecture, and data engineering, who can effectively convert experimental frameworks into dependable, business-ready systems.
This role demands extensive expertise in large language models (LLMs), retrieval-augmented generation (RAG), agentic AI workflows, CI/CD automation, and the management of production ML lifecycles within modern data platforms. The selected candidate will lead comprehensive AI projects, collaborate with both technical and business stakeholders, and uphold operational excellence across AI platforms.
Key Responsibilities:
- Design, develop, deploy, and maintain production-grade AI and machine learning systems from start to finish.
- Construct and enhance LLM-powered applications, including RAG pipelines, prompt workflows, agent-based systems, and multimodal AI applications.
- Create intelligent workflows through tool-calling, orchestration frameworks, and contextual reasoning models.
- Refine, assess, and operationalize machine learning and foundational models for enterprise applications.
- Build and oversee MLOps pipelines that encompass training, evaluation, model registration, deployment, monitoring, and retraining.
- Implement CI/CD pipelines for ML and AI workflows to facilitate automated testing, release management, and controlled deployments.
- Establish model monitoring frameworks to detect drift, attribute features, and track inference quality and performance.
- Ensure reproducibility, reliability, and version control across AI/ML environments.
- Architect scalable AI/ML platforms utilizing modern computing, storage, orchestration, monitoring, and search services.
- Develop reproducible environments employing Infrastructure as Code.
- Support secure, high-availability, and cost-effective deployment models throughout development, staging, and production phases.
- Design scalable inference and serving patterns adaptable to fluctuating workloads.
- Construct and maintain automated data pipelines, ETL/ELT workflows, and data processing frameworks for AI/ML utilization.
- Guarantee data quality, lineage, governance, and versioning to enable reliable model training and inference.
- Work with both structured and unstructured datasets across data lakes, data warehouses, and operational systems.
- Deliver analytics-ready datasets to downstream systems and applications.
- Lead multiple AI initiatives concurrently, including planning, execution, and coordination with internal teams and stakeholders.
- Collaborate closely with product, engineering, data, and business teams to deliver production-ready AI capabilities.
- Contribute to architectural decisions, technical documentation, and best practices across teams.
