Senior AI/ML Engineer – Service Assurance
Orange is hiring a Senior AI/ML Engineer – Service Assurance in Gurugram, India. Level rates it ; you can apply on Orange.
Level's read on this job
- Level rates this machine learning engineer role Builds AI ●●●●: the job is building AI systems.
- The posting mentions PyTorch, TensorFlow and scikit-learn among its tools and skills.
- Level classes it as a senior role.
- Orange has 857 other open roles on Level.
- Posted in the last few days.
AI in this role
Publication date : Apr 27, 2026, 12:00AM
Join us at Orange Business!
We are a network and digital integrator that understands the entire value chain of the digital world, freeing our customers to focus on the strategic initiatives that shape their business.
Every day, you will collaborate with a team dedicated to providing consistent, sustainable global solutions, no matter where our customers operate. With over 30,000 employees across Asia, the Americas, Africa, and Europe, we offer a dynamic environment to develop and perfect your skills in a field filled with exciting challenges and opportunities.
As a Senior AI/ML Engineer, you will be at the heart of transforming our Service Assurance capabilities.
Your mission will include (and not be limited to) leverage cutting-edge AI to predict and prevent service disruptions, automate root cause analysis, and ensure our network runs with unparalleled reliability.
You will be responsible for exploring service and network data, designing and implementing AI/ML and agentic capabilities on top of Splunk and Ciena Blue Planet UAA, and industrializing these solutions into production-grade features.
This role combines hands-on data exploration, model development, and solid software engineering to deliver advanced analytics for anomaly detection, event correlation, root cause analysis, and forecasting.
Key Responsibilities
1-Core Model Development & Innovation
- Design and Implement AI/ML Solutions:Own the end-to-end lifecycle of AI/ML models for service assurance, covering anomaly detection, event correlation, root cause analysis, and capacity/traffic forecasting.
This includes data discovery, feature engineering, model training, evaluation, and deployment. - Prototype and Industrialize Agentic AI:Explore and build next-generation operational tools, such as LLM-based copilots for triage, agents that reason over telemetry and topology, and natural-language interfaces for observability data.
- Stay at the Forefront of AIOps:Continuously research advances in ML for observability, proactively prototyping and industrializing innovative ideas that enhance our service-assurance capabilities.
2-Engineering & MLOps Excellence
- Build Scalable Data Pipelines:Engineer and maintain robust pipelines to ingest, transform, and prepare high-volume observability data (metrics, logs, alarms, tickets, topology) for AI/ML workloads.
- Implement MLOps Best Practices:Ensure our solutions are production-grade by implementing model versioning, automated testing, CI/CD for ML, and continuous monitoring for data and model drift.
- Deliver Reusable Capabilities:Expose AI/ML and agentic features as well-documented, reusable components that integrate seamlessly into dashboards, alerts, and operational workflows.
3-Strategy & Collaboration
- Translate Business Needs into AI Use Cases:Collaborate closely with the AI lead and service-assurance stakeholders to understand operational challenges and define high-impact AI/ML projects.
- Measure and Communicate Impact:Quantify the business value of your solutions (e.g., MTTR reduction, alarm noise reduction) and effectively communicate results and trade-offs to technical and non-technical partners.
Required Skills and Experience
1- Core Technical Skills
- Experience: 5+ years in software and/or ML engineering, with at least 3 years of hands-on experience building and deploying production AI/ML models.
- Programming: Strong proficiency in Python and its standard data science/ML libraries (e.g., Pandas, Scikit-learn, TensorFlow/PyTorch). Ability to write clean, testable, production-ready code.
- Data Engineering: Solid ExperiencewithSQL and at leastonedistributed dataprocessing framework(e.g., Spark, Flink) tohandlelarge-scale datasets.
- MLOps:Hands-on experience with MLOps tooling and methodologies (e.g., MLflow for versioning, CI/CD for model deployment, monitoring for drift).
2-AI/ML Expertise
- Machine Learning Foundations: Solid understanding of ML algorithms (especially for anomaly detection, classification, and time-series forecasting) and the ability to choose the right tool for the job.
- Feature Engineering: Proven ability to explore diverse data sources (logs, metrics, events, topology), assess data quality, and derive meaningful features.
- Agentic AI Concepts: A strong interest in and understanding of agentic AI concepts (LLM-based agents, tool-calling, reasoning) and a drive to apply them to real-world operational problems.
- Observability/AIOps Platforms: Familiarity with platforms like Splunk, ELK, or Prometheus for analyzing logs, metrics, and alerts.
3-Professional & Strategic Skills
- Problem Solving & Communication: Strong analytical skills to translate complex operational problems into concrete technical solutions. Ability to communicate effectively with both technical and non-technical stakeholders.
- Education: Master’s degree in Computer Science, Data Science, or a related field, or equivalent practical experience.
Nice-to-Have Skills
- Experience with Splunk or equivalent observability tooling in a telecom/IT operations context.
- Exposure to service assurance or network operations concepts (e.g., alarms, incidents, SLAs, topology, OSS/BSS).
You bring rigor, passion for challenges, and determination. You seek the opportunity to expand your expertise, achieve your goals, and thrive.
• Global Opportunities: Work in multi-national teams with opportunity to collaborate with colleagues and customers from all over the world.
• Flexible Work Environment: Flexible working hours and possibility to combine work from office and home (hybrid ways of working).
• Professional Development: training programs and upskilling/re-skilling opportunities.
• Career Growth: Internal growth and mobility opportunities within Orange.
• Caring and Daring Culture: Health and well-being programs and benefits, diversity & inclusion initiatives, CSR and employee connect events.
• Reward Programs: Employee Referral Program, Change Maker Awards.
At Orange, only your skills matter.
Regardless of your age, gender, background, origin, religion, sexual orientation, disability, neurodiversity, or appearance, we actively encourage diversity within our teams, as it is a collective strength and a driver of innovation.
Orange is a disability-inclusive employer: please feel free to let us know about any specific needs you may have.
How we rate this
Senior AI/ML Engineer – Service Assurance at Orange rates 93 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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Skills and AI tools this role asks for
Also common in machine learning engineer jobs at this level
Pay
- Typical machine learning engineer job
- $199k
This posting doesn't list pay. Use the typical machine learning engineer figure when pay comes up. See machine learning engineer jobs
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Walk me through how you've used PyTorch in your day-to-day work.
- What are the limits of TensorFlow that you've run into, and how did you work around them?
- What's a project where you used scikit-learn hands-on?
- Walk me through how you've used MLflow in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: ML Ops, PyTorch, TensorFlow, scikit-learn, and MLflow. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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