Key Deliverables

AI/ML Engineer I

- Cleaned, annotated, and pre-processed datasets for supervised learning models

- Simple machine learning models (e.g., logistic regression, decision trees) implemented under guidance

- Exploratory data analysis reports

- Jupyter notebooks documenting model experiments

- Unit-tested ML scripts

- Essential Duties and Responsibilities (All Levels):

- Assist in data cleaning, feature engineering, testing basic ML models, write and debug simple scripts

- Develop ML modules, assist in deployment, support data pipelines, contribute to documentation and unit testing

- Support data preparation, model training under guidance, debug code, attend knowledge sessions

- Develop and maintain smaller AI modules (e.g., anomaly detection), assist in deployments, write technical documentation

- Lead development of scalable ML models, integrate into ITSM systems, ensure compliance and performance metricsArchitect end-to-end AI platforms, oversee cross-domain projects (e.g., NLP for service desk, CV for asset tracking)

Education and/or Work Experience Requirements:

Minimum Requirements:

- Bachelor’s degree in Computer Science,Data Science, IT, or a related field.Master’s preferred or equivalent experience for senior levels

Preferred Certifications (All Levels):

- Google Cloud Professional Machine Learning Engineer

- AWS Certified Machine Learning – Specialty

- Microsoft Certified: Azure AI Engineer Associate

- TensorFlow Developer Certificate

- Databricks Certified Machine Learning Skilled

- Kubernetes or Docker certification for MLOps roles

- Knowledge, Skills & Abilities (KSAs):

- Machine Learning techniques (regression, classification, clustering)

- Deep Learning architectures (CNNs, RNNs, Transformers, LLMs)

- NLP (tokenization, BERT,



prompt engineering)

- Big Data fundamentals (Spark, Hadoop)

- Model interpretability, ethics in AI, bias detection

- Cloud-native AI services (AWS Sagemaker, GCP Vertex AI, Azure ML)

- Data governance, security, and ethical AI practices

Programming: Python, Apps Script

Frameworks: TensorFlow, PyTorch, scikit-learn, HuggingFace

Tools: Git, Docker, Kubernetes, Airflow, MLflow,Jupyter, Postman

Data pipeline skills: SQL, Pandas, data APIs

Deployment: Flask/FastAPI, CI/CD, REST APIs, cloud functions

- Strong analytical and debugging skills

- Translate business problems into AI solutions

- Communicate effectively with technical and non-technical stakeholders

- Work under Agile or DevOps-based workflows

- Stay current with research and emerging technologies

- Rapidly learn new AI concepts and tools

- Translate business challenges into ML solutions

- Communicate technical findings to non-technical stakeholders

- Handle ambiguity and balance research with delivery

- Collaborate across globally distributed teams

Technical Expertise

- Understands basic ML/DL principles

- Codes in Python/Apps Script

- Familiarity with AI/ML tools such as Jupyter, scikit-learn, or TensorFlow (basic use)

- Applies supervised/unsupervised ML methods

- Proficient in TensorFlow/PyTorch

- Uses cloud ML services

- Familiar with ML pipelines

- Documents technical solutions and contributes to code reviews

- Designs and builds production-grade models

- Uses MLflow, Airflow, CI/CD tools

- Experience with model deployment and monitoring

- Owns end-to-end AI/ML solutions including architecture, training, deployment, and monitoring

- Applies domain knowledge to improve model relevance (e.g., IT ops, cybersecurity)

- Understands data engineering best practices

📌 AI/ML Engineer I (India)
🏢 Astreya
📍 India