ML Engineer I

Role Description

We are seeking a visionary AI leader to head our AIOps function within the Intelligent Automation division. This role requires deep technical expertise, strong research orientation, and proven leadership capabilities to drive next-generation AI solutions for IT operations.

Key Responsibilities

AIOps Research & Innovation

Lead R&D initiatives in AIOps, focusing on predictive maintenance, anomaly detection, incident correlation, and root cause analysis
Explore and implement advanced AI approaches including Generative AI, Large Language Models (LLMs), and agentic AI

Project & Delivery Leadership

Own end-to-end delivery of AIOps AI solutions, ensuring alignment with business and client objectives
Drive scalable, production-ready implementations with high reliability and performance

Technical Leadership

Provide expert guidance across Machine Learning (ML), Deep Learning (DL), NLP, and Generative AI
Ensure best practices in model development, validation, deployment, and monitoring

Team Management

Lead, mentor, and grow a team of AI researchers, data scientists, and ML engineers
Foster a culture of innovation, collaboration, and continuous learning

Cross-functional Collaboration

Partner with product, engineering, and business teams to deliver impactful solutions
Translate business challenges into AI-driven opportunities

Strategic Contribution

Define AIOps roadmap, key milestones, and innovation strategy
Contribute to organizational AI vision and long-term capabilities

Qualifications

Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field (Master’s/Ph.D. preferred)
10+ years of experience in AI with strong exposure to enterprise-scale implementations
Proven experience in AIOps or IT operations analytics use cases

Must-Have Skills

Strong expertise in Machine Learning, Deep Learning, and NLP
Hands-on experience with Generative AI and LLMs (e.g., transformer-based models)
Proven experience in AIOps use cases (anomaly detection, predictive maintenance, incident management, RCA)
Proficiency in ML frameworks such as TensorFlow, PyTorch, or Scikit-learn
Solid understanding of MLOps practices (model deployment, monitoring, CI/CD for ML)
Experience with cloud platforms (Azure, AWS, or GCP)
Strong technical leadership and team management experience
Excellent problem-solving and analytical skills

Good-to-Have Skills

Experience with Databricks or similar data/AI platforms
Exposure to agentic AI systems and autonomous workflows
Knowledge of ITSM tools and IT operations environments
Familiarity with real-time data processing and streaming frameworks
Experience in multi-cloud or hybrid cloud environments
Contributions to research publications, patents, or open-source AI projects
Understanding of AI governance, ethics, and responsible AI practices

Key Traits

Strategic thinker with a strong innovation mindset
Ability to operate in fast-paced, dynamic environments
Strong communication and stakeholder management skills
Results-driven with a focus on quality and scalability

Skills

Machine Learning, Artificial Intelligence, Deep Learning, Python.

Key Skills

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