AI Engineer (Agentic Framework & Cloud FinOps Domain)

Experience : 5 years

NP : Immediate joiner

Location : Bangalore (Hybrid)

About the Role

CirrusLabs is expanding its advanced AI engineering capability within a cloud-first, infrastructure-heavy environment. We are seeking an AI Engineer with hands-on experience in Agentic AI frameworks (multimodal) and strong understanding of Cloud FinOps . This role will architect next-generation AI systems that automate cloud operations, optimize cost efficiency, and drive intelligence across enterprise-scale platforms.

Key Responsibilities

Design, build, and deploy agentic AI systems using multimodal models (text, images, telemetry, structured datasets).

Develop AI-driven automation workflows that optimize cloud resources, utilization, and FinOps governance.

Partner with FinOps, Cloud Engineering, and Infrastructure teams to translate consumption data into actionable AI recommendations.

Build scalable inference pipelines and integrate AI agents into CirrusLabs internal and client-facing platforms.

Implement monitoring, observability, and AI governance controls to ensure reliable model performance.

Evaluate new frameworks, LLM technologies, and agentic orchestration tools to continuously enhance AI capabilities.

Collaborate with globally distributed engineering teams across US, LATAM, and India.

Required Qualifications

5+ years in AI/ML engineering with hands-on experience using Agentic AI frameworks (AutoGen, CrewAI, LangChain Agents, OpenAI Agents, etc.).

Expertise in multimodal models and vector embeddings for real-time decisioning.

Strong understanding of Cloud FinOps principles , including cost modeling, chargeback/showback, optimization strategies, and cloud economics.

Experience deploying and scaling AI solutions in AWS, Azure, or GCP environments.

Proficiency in Python and modern AI engineering tools.

Demonstrated ability to build scalable pipelines and AI agent workflows.

Preferred Qualifications

Experience supporting cloud platform operations or infrastructure teams.

Background working with FinOps tools such as Cloudability, CloudHealth, or native cloud billing dashboards.

Understanding of MLOps (feature stores, CI/CD for models, monitoring, governance).

2. Machine Learning Engineer (Recommendation Systems + Cloud Costing Domain)

Employer: CirrusLabs

Employment Type: Full-Time

About the Role

CirrusLabs is seeking a Machine Learning Engineer to build cloud cost-optimization recommendation systems and predictive models at scale. This role will leverage modern AI and ML frameworks to deliver intelligent insights that guide cloud spending, workload optimization, and operational efficiency for global customers.

Key Responsibilities

Build recommendation models that optimize cloud resource allocation, predict usage patterns, and reduce cost inefficiencies.

Create predictive and prescriptive ML solutions based on cloud billing, utilization, and operational datasets.

Design and maintain end-to-end ML pipelines, including data ingestion, feature engineering, training, tuning, deployment, and monitoring.

Collaborate with Cloud FinOps, Infrastructure, and Platform Engineering teams to operationalize ML-driven insights.

Develop reusable ML components, templates, and libraries for cloud cost management.

Ensure ML models follow governance, compliance, and best practices across all regions.

Evaluate emerging ML algorithms and AI tools to enhance CirrusLabs cloud optimization capabilities.

Required Qualifications

5+ years of ML engineering experience with a strong background in recommendation systems or predictive modeling.

Experience building ML solutions for cloud costing, resource optimization, or consumption forecasting .

Hands-on experience with AI/ML libraries: TensorFlow, PyTorch, Scikit-learn, XGBoost, or similar.

Solid understanding of AWS, Azure, or GCP cloud infrastructure and pricing models.

Strong proficiency in Python and API-based model integration.

Experience building scalable machine learning pipelines with MLOps best practices.

Preferred Qualifications

Background supporting FinOps programs or cloud cost management teams.

Experience integrating LLM-derived features into ML models or hybrid AI+ML systems.

Familiarity with Databricks, Azure ML, SageMaker, or Vertex AI