Senior Consultant Machine Learning Engineering

Responsibilities

What You’ll Do

As a Mid Level Machine Learning Engineer on our GenAI Enablement team, you'll work at the forefront of Large Language Model Operations (LLMOps), helping to design, develop, and scale our AI capabilities. You'll join our community of engineers to build solutions that transform how we serve our customers through AI technology.

Understand the business problem, the data science solutions and operationalize it to deliver outcomes at level of scale/efficiency, integrate with other systems. This role is mainly comprising of three aspects: Data acquisition with a focus on CI/CD (continuous integration/continuous deployment), Model orchestration & deployment & Governance and operational support.

Data Acquisition

• Work with Data Engineering team to understand and help develop build-as-per-need infrastructure for Data collection and ETL processes, automate steps in ETL & develop system to manage, deploy and maintain Data Engineering code. Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry leader. Work with data and analytics experts to strive for greater functionality in our data systems (including feature engineering).

Model Orchestration & Deployment

• Assist in the development of systems to manage, deploy and maintain ML code. Work closely with the Data Sciences team to develop infrastructure in order for Machine Learning models to be deployed, take over newly developed models into production, develop systems for integrating AI/ML components using orchestration services. Build CI/CD pipelines interconnecting Data services and ML services for the project with an aim to achieve MLOps. Assist in development and implementation of ML toolchains and data platforms to scale ML solutions in production.

Governance and Operational Support

• Enable the agility in data science delivery through automation across build, validation, deployment and monitoring of Data Science models. Monitor quality parameters for ML models in production. Shape and operate best practices for managing models in production. Contribute to solutions that accelerate the task of Production issue analysis by Data Scientists by enabling log viewing, tracing and debugging of data science features in production.

Qualifications

Who You Are

• Bachelor's Engineering degree – preference in a computer science, technology, engineering or math-related field
• 12+ years leading software/application development projects with strong delivery ownership
• Strong programming skills in AWS, Python (infrastructure development such as Lambda, application development rather than data science)
• ML Engineering skills with software engineer/infra structure engineer with ML/AI background
• Platform Ownership: Design and operate the ML platform (training, inference, orchestration, observability, governance)
• Delivery & Reliability: Establish SLAs/SLOs for model services; ensure uptime, scalability, and disaster recovery
• MLOps Pipelines: Build/standardize CI/CD/CT for data + models + infra
• Model Governance: Champion reproducibility, lineage, approvals, auditability, responsible AI, and compliance-by-default
• Cost Management: Optimize cloud spend (compute/storage), autoscaling, and GPU allocation
• Security & Risk: Secrets management, IAM, network policies, data privacy and model security
• People Leadership: Hiring, coaching, technical direction, delivery rituals, stakeholder engagement
• Change Management: Drive adoption, enablement, and documentation of best practices

Skills That Will Help You Stand Out

• Deployed & operated multiple production models (batch + real-time) with monitoring and rollback
• Kubernetes, Terraform, registry & pipeline orchestration, and CI/CD for ML
• Cloud-native stack (one major cloud) and infra security basics
• Proven people leadership (hiring, mentoring, roadmap ownership)
• GenAI/LLMOps with RAG and LLM evals, Guardrails