Role Description
Role Proficiency:Independently interprets data and analyses results using statistical techniques
Outcomes
- Independently Mine and acquire data from primary and secondary sources and reorganize the data in a format that can be easily read by either a machine or a person; generating insights and helping clients make better decisions.
- Develop reports and analysis that effectively communicate trends patterns and predictions using relevant data.
- Utilizes historical data sets and planned changes to business models and forecast business trends
- Working alongside teams within the business or the management team to establish business needs.
- Creates visualizations including dashboards flowcharts and graphs to relay business concepts through visuals to colleagues and other relevant stakeholders.
- Set FAST goals
Measures Of Outcomes
- Schedule adherence to tasks
- Quality – Errors in data interpretation and Modelling
- Number of business processes changed due to vital analysis.
- Number of insights generated for business decisions
- Number of stakeholder appreciations/escalations
- Number of customer appreciations
- No: of mandatory trainings completed
Outputs Expected
Data Mining:
- Acquiring data from various sources
Reorganizing/Filtering Data
- Consider only relevant data from the mined data and convert it into a format which is consistent and analysable.
Analysis
- Use statistical methods to analyse data and generate useful results.
Create Data Models
- Use data to create models that depict trends in the customer base and the consumer population as a whole
Create Reports
- Create reports depicting the trends and behaviours from the analysed data
Document
- Create documentation for own work as well as perform peer review of documentation of others' work
Manage Knowledge
- Consume and contribute to project related documents share point libraries and client universities
Status Reporting
- Report status of tasks assigned
- Comply with project related reporting standards and process
Code
- Create efficient and reusable code. Follows coding best practices.
Code Versioning
- Organize and manage the changes and revisions to code. Use a version control tool like git bitbucket etc.
Quality
- Provide quality assurance of imported data working with quality assurance analyst if necessary.
Performance Management
- Set FAST Goals and seek feedback from supervisor
Skill Examples
- Analytical Skills: Ability to work with large amounts of data: facts figures and number crunching.
- Communication Skills: Ability to present findings or translate the data into an understandable document
- Critical Thinking: Ability to look at the numbers trends and data; coming up with new conclusions based on the findings.
- Attention to Detail: Making sure to be vigilant in the analysis to come with accurate conclusions.
- Quantitative skills - knowledge of statistical methods and data analysis software
- Presentation Skills - reports and oral presentations to senior colleagues
- Mathematical skills to estimate numerical data.
- Work in a team environment
- Proactively ask for and offer help
Knowledge Examples
Knowledge Examples
- Proficient in mathematics and calculations.
- Spreadsheet tools such as Microsoft Excel or Google Sheets
- Advanced knowledge of Tableau or PowerBI
- SQL
- Python
- DBMS
- Operating Systems and software platforms
- Knowledge about customer domain and also sub domain where problem is solved
- Code version control e.g. git bitbucket etc
Additional Comments
About the Role We are looking for a skilled and forward-thinking Cloud AI/ML Engineer to design, develop, and support scalable, secure, and high-performance generative AI applications on AWS. This role will work at the intersection of cloud engineering and artificial intelligence, enabling efficient delivery of state-of-the-art AI capabilities using services like Amazon Bedrock and SageMaker. You’ll be part of a collaborative team working on cutting-edge generative AI projects, and you’ll play a key role in implementing cloud-native solutions with best practices in infrastructure automation, security, and observability. Key Responsibilities
- AI/ML Integration o Leverage Amazon Bedrock for foundation models and SageMaker for custom model training and deployment. o Build and maintain generative AI applications that use AWS-native AI/ML services efficiently.
- Deployment & Operations o Develop robust CI/CD pipelines for automating infrastructure deployment and AI model lifecycle management. o Implement real-time monitoring and logging using Amazon CloudWatch and other observability tools. o Ensure availability and reliability of AI systems in production environments.
- Security & Compliance o Apply AWS IAM, encryption, and other best practices to protect data and models. o Ensure compliance with organizational and industry-specific data protection standards.
- Collaboration & Support o Work closely with data scientists, machine learning engineers, and product owners to translate requirements into robust solutions. o Troubleshoot and resolve issues related to model performance, infrastructure, and AWS services.
- Optimization & Documentation o Continuously evaluate and optimize model performance and cloud infrastructure for cost and efficiency. o Document infrastructure, deployment workflows, and best practices for team use and knowledge sharing.
- Mentorship & Guidance o Share knowledge of AWS services and generative AI best practices with peers and junior engineers. Required Skills & Experience
- Proficiency in AWS services, especially EC2, SageMaker, Bedrock, and IAM.
- Strong programming skills in Python and experience with containerization using Docker.
- Familiarity with Kubernetes for container orchestration.
- Experience building and maintaining CI/CD pipelines for AI applications and MLOps
- Strong understanding of data security, compliance, and monitoring tools in AWS.
- Hands-on experience managing databases and data flows in cloud environments. Preferred Qualifications
- AWS certifications (e.g., AWS Certified Machine Learning – Specialty, AWS DevOps Engineer).
- Experience with responsible AI practices for generative models.
- Exposure to cost optimization and resource scaling strategies in production AI workloads.
Skills
Aws,Python,Ai
