Senior AI Platform Engineer (MLOps & Data Science Infrastructure
Summary
Builds and maintains end-to-end MLOps infrastructure and AI pipelines — including LLM/Generative AI platforms — so ML models (churn, pricing, anomaly detection, GenAI) run reliably in real-time enterprise workflows. Core stack: Python, SQL, Spark, MLflow/Airflow/Kubeflow, cloud (Azure/AWS/GCP), Docker, Kubernetes.
Position Summary
As a Senior AI Platform Engineer, you will bridge the gap between Data Science research and scalable, enterprise-grade AI production systems. You will design, build, and maintain the end-to-end MLOps infrastructure and decision engines that allow advanced AI/ML models-ranging from predictive analytics (churn, pricing optimization, anomaly detection) to Generative AI and Large Language Models (LLMs)-to run seamlessly in real-time enterprise workflows.This role is ideal for a candidate with strong data science, machine learning, and software engineering foundations who thrives on building scalable AI pipelines, automating model lifecycle management, and empowering crossfunctional teams across Marketing, Enterprise, Network, and Operations.
Key Responsibilities
1. AI Pipeline & MLOps Infrastructure Development
Productionize ML Workflows: Architect, build, and deploy end-toend AI/ML pipelines and customer-centric decision engines that integrate seamlessly into enterprise applications and customer journeys.
Model Lifecycle Management: Implement robust MLOps practices, including automated model training, orchestration, deployment, versioning, monitoring, and continuous integration/continuous deployment (CI/CD) for ML workflows.
LLM & Generative AI Platforming: Build infrastructure supporting fine-tuning, evaluation frameworks, prompt engineering pipelines, redteaming, and real-time inference monitoring for Large Language Models (e.g., GPT, LLaMA).
2. Model Governance, Quality & Optimization Governance & Observability: Drive best practices around model governance, explainability, lineage tracking, and performance monitoring to prevent model drift and ensure compliance with enterprise policies and regulations.
Scalable Data Systems: Process and manipulate large-scale multidomain datasets (network, customer, financial, operational) to support real-time inference, anomaly detection, digital twins, and optimization models.
Experimentation Frameworks: Build and optimize continuous experimentation environments, enabling scalable A/B testing, uplift modeling, causal inference testing, and dynamic feature engineering.
3. Engineering Excellence & Collaboration Software Engineering Best Practices: Champion high code quality, modular software design, reproducibility, automated testing, and version control standards across the team.
Technical Mentorship: Provide technical guidance, pair programming, and constructive code reviews for junior team members to raise overall engineering capability.
Cross-Functional Partnering: Translate complex enterprise business challenges (such as pricing optimization, churn prevention, and resource allocation) into scalable AI platform architectures.
Skills & Qualifications Bachelor's or Postgraduate degree in Computer Science, Data Science, Mathematics, Statistics, Software Engineering, or a related quantitative field.
At least 3-5 years of hands-on experience developing, deploying, and maintaining production machine learning models, MLOps platforms, or scalable data science pipelines.
Technical Capabilities ML & Generative AI Frameworks: Deep understanding of machine learning algorithms (supervised learning, time series forecasting, neural networks, clustering) alongside hands-on experience finetuning, serving, and evaluating LLMs.
MLOps & Lifecycle Tools: Proficiency with ML orchestration and tracking tools (e.g., MLflow, Airflow, Kubeflow, Databricks).
Data Processing & Engineering: Expertise in Python, SQL, Spark, and big data technologies (Hadoop/Hive) for efficient data manipulation and feature engineering.
Cloud & DevOps Platforming: Hands-on experience with major cloud platforms (Azure, AWS, or GCP) and software development tools (Git, GitHub, GitLab, Docker, Kubernetes).
Code & Architecture Quality: Solid foundation in modular coding, unit/integration testing, CI/CD practices, and scalable system design.
Model Lifecycle Management: Implement robust MLOps practices, including automated model training, orchestration, deployment, versioning, monitoring, and continuous integration/continuous deployment (CI/CD) for ML workflows.
LLM & Generative AI Platforming: Build infrastructure supporting fine-tuning, evaluation frameworks, prompt engineering pipelines, redteaming, and real-time inference monitoring for Large Language Models (e.g., GPT, LLaMA).
2. Model Governance, Quality & Optimization Governance & Observability: Drive best practices around model governance, explainability, lineage tracking, and performance monitoring to prevent model drift and ensure compliance with enterprise policies and regulations.
Scalable Data Systems: Process and manipulate large-scale multidomain datasets (network, customer, financial, operational) to support real-time inference, anomaly detection, digital twins, and optimization models.
Experimentation Frameworks: Build and optimize continuous experimentation environments, enabling scalable A/B testing, uplift modeling, causal inference testing, and dynamic feature engineering.
3. Engineering Excellence & Collaboration Software Engineering Best Practices: Champion high code quality, modular software design, reproducibility, automated testing, and version control standards across the team.
Technical Mentorship: Provide technical guidance, pair programming, and constructive code reviews for junior team members to raise overall engineering capability.
Cross-Functional Partnering: Translate complex enterprise business challenges (such as pricing optimization, churn prevention, and resource allocation) into scalable AI platform architectures.
Skills & Qualifications Bachelor's or Postgraduate degree in Computer Science, Data Science, Mathematics, Statistics, Software Engineering, or a related quantitative field.
At least 3-5 years of hands-on experience developing, deploying, and maintaining production machine learning models, MLOps platforms, or scalable data science pipelines.
Technical Capabilities ML & Generative AI Frameworks: Deep understanding of machine learning algorithms (supervised learning, time series forecasting, neural networks, clustering) alongside hands-on experience finetuning, serving, and evaluating LLMs.
MLOps & Lifecycle Tools: Proficiency with ML orchestration and tracking tools (e.g., MLflow, Airflow, Kubeflow, Databricks).
Data Processing & Engineering: Expertise in Python, SQL, Spark, and big data technologies (Hadoop/Hive) for efficient data manipulation and feature engineering.
Cloud & DevOps Platforming: Hands-on experience with major cloud platforms (Azure, AWS, or GCP) and software development tools (Git, GitHub, GitLab, Docker, Kubernetes).
Code & Architecture Quality: Solid foundation in modular coding, unit/integration testing, CI/CD practices, and scalable system design.