Senior Consultant - Machine Learning Engineering

Responsibilities
What Youll Do
As a Mid Level Machine Learning Engineer on our GenAI Enablement team, youll work at the forefront of Large Language Model Operations (LLMOps), helping to design, develop, and scale our AI capabilities. Youll 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 to 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

• Bachelors 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 (Python programming skills from infrastructure development like lambda, App dev 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 (continuous training) 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.

  Looking to get Placed? Try our Placement Guarantee Plan

  Get Hired
• 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.
  

Additional Information
Our Engineering Culture
Through our Agile/Lean DevOps environment dedicated to delivering valuable solutions, weve fostered a culture of innovation and experimentation across our development teams. As a customer-focused organization, we work closely with our end users and product owners to understand and rapidly respond to emerging business needs.
Collaboration is embedded into everything we do – from the products we develop to the quality service we provide. Were driven by the belief that diversity of thought, background, and perspective is critical to creating the best products and experiences for our customers.