AI/ML Engineer III

About the Role

As an AI/ML Engineer III at Astreya, you will be instrumental in bridging the gap between business objectives and cutting-edge machine learning solutions. You will lead the translation of ambiguous product needs into clear ML metrics and success criteria, owning the entire lifecycle from prototyping (including deep learning and GenAI) to deployment and monitoring. Your expertise will be crucial in developing and maintaining observability dashboards, running and safeguarding models in real-time, and championing cross-functional collaboration and governance. You will also pilot new ML tools and frameworks, leading their integration into production, and architecting data strategies that emphasize reproducibility, traceability, and quality across the ML stack.

Key Deliverables by Level

• Level 3: AI/ML Engineer III
• Scalable ML pipelines with automated training, validation, and deployment workflows.
• Deployed ML solutions integrated with Astreya’s managed service platforms (e.g., NLP for ticket routing).
• Dashboards for monitoring inference quality and data drift.
• MLOps pipelines with CI/CD practices.

Essential Duties and Responsibilities

Your responsibilities will span across various levels, focusing on the full spectrum of AI/ML development:

• Assisting in data cleaning, feature engineering, testing basic ML models, and writing/debugging simple scripts.
• Developing ML modules, assisting in deployment, supporting data pipelines, and contributing to documentation and unit testing.
• Supporting data preparation, model training under guidance, debugging code, and attending knowledge sessions.
• Developing and maintaining smaller AI modules (e.g., anomaly detection), assisting in deployments, and writing technical documentation.
• Leading the development of scalable ML models, integrating them into ITSM systems, and ensuring compliance and performance metrics.
• Architecting end-to-end AI platforms and overseeing cross-domain projects (e.g., NLP for service desk, CV for asset tracking).
• Leading ML solution design, owning production deployments, optimizing inference models, and driving MLOps practices.
• Architecting end-to-end solutions for AI-driven services (e.g., IT ticket routing, network anomaly detection) and leading AI projects.

Minimum Requirements

• Bachelor’s degree in Computer Science, Data Science, IT, or a related field. Master’s degree or equivalent experience is preferred for senior levels.
• Level 3 requires 4–6 years of experience in ML/AI implementation and deployment.

Preferred Certifications

• Google Cloud Professional Machine Learning Engineer
• TensorFlow Developer Certificate

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.
• AI Ethics: Model interpretability, ethics in AI, bias detection.
• Cloud-native AI Services: GCP Vertex AI.
• Data Governance: Security, and ethical AI practices.
• Programming: Python, Apps Script, SQL.
• 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.
• Skills: Strong analytical and debugging skills, ability to translate business problems into AI solutions, effective communication with technical and non-technical stakeholders, experience with Agile or DevOps workflows, continuous learning of research and emerging technologies, rapid learning of new AI concepts and tools, handling ambiguity, balancing research with delivery, and collaborating across globally distributed teams.

Key skills/competency

• Machine Learning
• Deep Learning
• GenAI
• NLP
• MLOps
• Python
• TensorFlow
• PyTorch
• Data Strategy
• Cloud AI Services