AI/ML Engineer I

Overview

Design, develop, and implement artificial intelligence and machine learning models to solve enterprise IT and operational challenges. At Level 1, contribute to data preparation, basic machine learning models, exploratory analysis, experimentation, and unit-tested ML scripts under guidance.

What you'll do

• Translate business goals into measurable ML goals, including KPIs and acceptance thresholds, in collaboration with PMs and data scientists.
• Own the full lifecycle from prototyping, including deep learning and GenAI, to deployment and monitoring.
• Develop and maintain observability dashboards and alerts tied to ML metrics and feature drift.
• Run and safeguard models in real time.
• Pilot new ML tools and frameworks, leading integration into production where appropriate.
• Act as an ML thought leader across product, infrastructure, legal, and UX on responsible ML.
• Clean, annotate, and preprocess datasets for supervised learning models.
• Implement simple machine learning models, such as logistic regression and decision trees, under guidance.
• Prepare exploratory data analysis reports.
• Document model experiments in Jupyter notebooks.
• Write unit-tested ML scripts.
• Assist in data cleaning, feature engineering, testing basic ML models, and writing and debugging simple scripts.
• Develop ML modules, assist in deployment, support data pipelines, and contribute to documentation and unit testing.
• Support data preparation and model training under guidance, debug code, and attend knowledge sessions.
• Develop and maintain smaller AI modules, such as anomaly detection, assist in deployments, and write technical documentation.
• Lead development of scalable ML models, integrate them into ITSM systems, and ensure compliance and performance metrics.
• Architect end-to-end AI platforms and oversee cross-domain projects, such as NLP for service desk and computer vision for asset tracking.
• Translate business problems and challenges into AI and ML solutions.
• Communicate technical findings to non-technical stakeholders.
• Handle ambiguity and balance research with delivery.
• Collaborate across globally distributed teams.

What you'll need

• Bachelor's degree in Computer Science, Data Science, IT, or a related field.
• 1–2 years in data science/ML roles.
• Hands-on experience with frameworks like scikit-learn or PyTorch.
• Python is required.
• SQL and Apps Script.
• Strong analytical and debugging skills.
• Ability to communicate effectively with technical and non-technical stakeholders.
• Ability to work under Agile or DevOps-based workflows.
• Ability to stay current with research and emerging technologies.
• Ability to rapidly learn new AI concepts and tools.
• Ability to analyze and clean datasets.
• Good communication and team collaboration skills.
• Ability to solve small coding and data cleaning problems.
• Ability to identify root causes in data and model issues.
• Ability to apply ML solutions to scoped problems.
• Effective debugging and troubleshooting of code and data issues.
• Ability to understand basic ML/DL principles.
• Ability to code in Python/R.
• Familiarity with AI/ML tools such as Jupyter, scikit-learn, or TensorFlow at a basic-use level.
• Ability to contribute to documentation and code reviews.
• Ability to share ideas in meetings and communicate findings clearly to peers.
• Ability to contribute to documentation and demos.

Nice to have

• Master's degree or equivalent experience for senior levels.
• Java/C++.
• TensorFlow, PyTorch, scikit-learn, and HuggingFace.
• Git, Docker, Kubernetes, Airflow, MLflow, Jupyter, and Postman.
• Data pipeline skills including Pandas and data APIs.
• Deployment experience with Flask/FastAPI, CI/CD, REST APIs, and cloud functions.
• Experience using cloud ML services.
• Familiarity with ML pipelines.
• Experience designing and building production-grade models.
• Experience using MLflow, Airflow, and CI/CD tools.
• Experience with model deployment and monitoring.
• Ability to own end-to-end AI/ML solutions including architecture, training, deployment, and monitoring.
• Ability to apply domain knowledge to improve model relevance, such as IT operations and cybersecurity.
• Experience driving model optimization at scale.
• Understanding of data engineering best practices.
• Ability to define organization-wide AI/ML standards.
• Ability to oversee architecture for reusable platforms.
• Ability to direct ML model governance and compliance.
• Ability to evaluate and mitigate risks related to fairness, privacy, and regulatory requirements.
• Ability to select and tune algorithms for real-world impact.
• Ability to innovate within a team on novel use cases.
• Ability to collaborate cross-functionally to integrate models into services.
• Ability to explain model behavior to technical and semi-technical audiences.
• Ability to interpret results and present actionable insights to stakeholders.
• Ability to build trust with cross-functional teams and leadership.

Details

• Location: India.
• Work mode: Remote.
• The role is Level 1 within a progression from levels 1–5 in complexity, autonomy, and responsibility.