Note: The job is a remote job and is open to candidates in USA. Motion Recruitment is seeking a highly skilled, execution-focused Senior AI Engineer to join its Transformation Office. This role will take ownership of the production lifecycle of enterprise AI initiatives, operationalizing AI at scale and ensuring that large language model applications and traditional machine learning models are deployable and scalable within a multi-cloud environment.
Responsibilities
- Build and maintain automated CI/CD and CT (Continuous Training) pipelines across AWS (SageMaker/Bedrock) and Azure (AI Studio)
- Design and execute the infrastructure for Retrieval-Augmented Generation (RAG), including vector database management (OpenSearch, Pinecone, or Azure AI Search) and semantic index optimization
- Build the engineering "pipes" to securely ingest and move data from legacy systems (Mainframes, SQL Server, on-prem DBs) into cloud-native MLOps workflows
- Implement systemized frameworks for LLM evaluation (LLM-as-a-judge, ROUGE, METEOR) and traditional ML validation to ensure performance before deployment
- Deploy real-time monitoring for model drift, hallucination detection, latency, and token consumption to manage both quality and cost
- Manage all AI resources using Terraform or CloudFormation, ensuring the cloud posture is reproducible, secure, and follows a "Privacy by Design" mandate
- Partner with teams using platforms like Palantir, Databricks, or Snowflake to ensure a high-fidelity data flow between analytical ontologies and production models
- Work directly with central IT and Security to navigate IAM roles, VPC peering, and firewall configurations, clearing the path for rapid transformation
- Optimize model serving endpoints for high-throughput and low-latency, utilizing containerization (Docker/Kubernetes) and serverless architectures where appropriate
- Establish rigorous version control for prompts (PromptOps), model weights, and data snapshots to ensure 100% auditability and rollback capability
- Support the data science lifecycle by automating feature stores, feature engineering pipelines, and the transition of experimental notebooks into hardened production microservices
- Implement automated scanning and guardrails (e.g., Bedrock Guardrails or Azure Content Safety) to prevent prompt injection and data leakage
Skills
- Bachelor's degree in Computer Science or a related field required
- 6+ years of engineering experience, with a minimum of 3 years strictly focused on MLOps or LLMOps in a production environment
- AWS & Azure Mastery: Deep, hands-on proficiency in both ecosystems. You must be able to configure Bedrock and Azure OpenAI services, including private networking and endpoint security, on day one
- Technical Stack: Expert Python, SQL, and PySpark. Extensive experience with containerization (Docker, Kubernetes) and orchestration tools (Airflow, Kubeflow, or Step Functions)
- LLM Tooling: Professional experience with evaluation and observability frameworks like LangSmith, Arize Phoenix, or WhyLabs
- Data Science Flavor: A strong understanding of statistical validation, model evaluation metrics, and the ability to partner with Data Scientists to optimize model performance
- Transformation Mindset: The ability to move at the speed of a startup while maintaining the collaborative relationships required to function within a large-scale enterprise IT landscape
- Master's degree in a quantitative discipline
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