- Location
- India
- Job type
- Full-time
Required skills
- Python
- Agile
- Airflow
- C++
- compliance
- cross-functionally
- data science
- decision trees
- deep learning
- Docker
- end-to-end
- FastAPI
- Flask
- Git
- Java
- Kubernetes
- machine learning
- NLP
- Pandas
- regression
- service desk
- SQL
- team collaboration
- TensorFlow
- ServiceNow
- Pytorch
- REST APIs
About the role
Astreya
Website:
astreya.com
Job details:
Scope
- Translate business goals into measurable ML goals (KPIs, acceptance thresholds) in collaboration with PMs and data scientists.
- Own the full lifecycle from prototyping (incl. 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/frameworks, leading integration into production where appropriate.
- Act as a cross-org ML thought leader—aligning product, infra, legal, and UX on responsible ML.
Key Deliverables by Level
Level 1
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)
Minimum Requirements
Education and/or Work Experience Requirements
- Bachelor’s degree in Computer Science,Data Science, IT, or a related field.Master’s preferred or equivalent experience for senior levels
- Level 1: 1–2 years in data science/ML roles; hands-on with frameworks like scikit-learn or PyTorch
- Programming: Python (must), Java/C++ (optional), SQL, Apps Script, ServiceNow
- 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
Competencies
- Each level, 1 - 5, represents a progression in complexity, autonomy, and responsibility. The higher the level, the more critical thinking, leadership, and expertise are required.
- Technical Expertise
- Understands basic ML/DL principles
- Codes in Python/R
- 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)
- Drives model optimization at scale
- Understands data engineering best practices
- Defines org-wide AI/ML standards
- Oversees architecture for reusable platforms
- Directs ML model governance and compliance
- Evaluates and mitigates risks related to fairness, privacy, and regulatory requirements
- Problem Solving & Innovation
- Solves small coding and data cleaning problems
- Ability to analyze and clean datasets
- Identifies root causes in data/model issues
- Applies ML solutions to scoped problems
- Effective in debugging and troubleshooting code and data issues
- Selects and tunes algorithms for real-world impact
- Innovates within team on novel use cases
Collaboration & Communication
- Good communication and team collaboration skills
- Shares ideas in meetings
- Communicates findings clearly to peers
- Contributes to documentation and demos
- Collaborates cross-functionally to integrate models into services
- Explains model behavior to technical and semi-technical audiences
- Interprets results and presents actionable insights to stakeholders
- Builds trust with cross-functional teams and leadership
Click on Apply to know more.
This page is fully interactive when JavaScript is enabled. Please enable JavaScript to apply or browse related roles.