The ELP AI Engineer supports the design development and day-to-day operation of AI and data science solutions for an assigned business function (e.g. Manufacturing Marketing Logistics). Working under the guidance of the Lead AI and senior engineers the role contributes hands-on to building and maintaining solutions across techniques such as machine learning deep learning natural language processing computer vision generative AI and robotic process automation.
The core purpose is execution and learning: writing clean and efficient code preparing and validating data assisting with proof-of-concept builds and helping keep deployed models running reliably. Every contribution is oriented toward two outcomes creating measurable business value and improving cost efficiency so the engineer learns early to connect technical work to real operational impact within the function.
4) Key Result Areas:Writethe key results expected from the job and the supporting actions for each of these key result areas (For a majority of jobs typically there could be 4- 7 key result areas)
Key Result Areas
Supporting Actions
AI & Data Science Development
Develop test and help deploy AI/ML components under guidance spanning machine learning deep learning NLP computer vision generative AI and RPA to address function-specific problems.
Write clean well-documented and efficient code that can be integrated into larger production-ready solutions.
Assist with testing and deployment phases supporting quality assurance and operational readiness of solutions built by the team.
Proof-of-Concept & Prototyping Support
Support the build of Proofs of Concept and prototypes to validate new AI approaches contributing modules experiments and analysis as assigned.
Help document PoC results clearly so the team can articulate business applicability and value.
Data Preparation Integration & Quality
Assist in building and maintaining data pipelines that ingest data from sensors MES ERP CRM and enterprise databases.
Perform data cleaning validation and basic feature engineering to ensure high-quality consistent datasets for model development.
Follow data governance and security standards in all data handling.
Operational Support & Monitoring
Support live AI systems by monitoring performance running routine checks and helping resolve assigned incidents.
Track basic performance metrics and flag anomalies or opportunities for improvement to senior team members.
Learn and apply MLOps practices version control CI/CD automated testing and model monitoring as part of the delivery workflow.
Collaboration & Communication
Work closely with senior engineers data teams and function stakeholders to understand requirements and deliver assigned tasks.
Provide clear timely updates on task progress risks and blockers.
Governance Compliance & Ethical AI
Adhere to corporate data governance cybersecurity regulatory and ethical AI (Responsible AI / DPDP) guidelines in all work.
Continuous Learning & Capability Building
Actively build skills across the AI toolkit tools and internal platforms and share learnings with the team.
Contribute to reusable assets documentation and internal knowledge repositories.
Required Experience:
Manager
1) Job Purpose: The ELP AI Engineer supports the design development and day-to-day operation of AI and data science solutions for an assigned business function (e.g. Manufacturing Marketing Logistics). Working under the guidance of the Lead AI and senior engineers the role contributes hands-on to ...
1) Job Purpose:
The ELP AI Engineer supports the design development and day-to-day operation of AI and data science solutions for an assigned business function (e.g. Manufacturing Marketing Logistics). Working under the guidance of the Lead AI and senior engineers the role contributes hands-on to building and maintaining solutions across techniques such as machine learning deep learning natural language processing computer vision generative AI and robotic process automation.
The core purpose is execution and learning: writing clean and efficient code preparing and validating data assisting with proof-of-concept builds and helping keep deployed models running reliably. Every contribution is oriented toward two outcomes creating measurable business value and improving cost efficiency so the engineer learns early to connect technical work to real operational impact within the function.
4) Key Result Areas:Writethe key results expected from the job and the supporting actions for each of these key result areas (For a majority of jobs typically there could be 4- 7 key result areas)
Key Result Areas
Supporting Actions
AI & Data Science Development
Develop test and help deploy AI/ML components under guidance spanning machine learning deep learning NLP computer vision generative AI and RPA to address function-specific problems.
Write clean well-documented and efficient code that can be integrated into larger production-ready solutions.
Assist with testing and deployment phases supporting quality assurance and operational readiness of solutions built by the team.
Proof-of-Concept & Prototyping Support
Support the build of Proofs of Concept and prototypes to validate new AI approaches contributing modules experiments and analysis as assigned.
Help document PoC results clearly so the team can articulate business applicability and value.
Data Preparation Integration & Quality
Assist in building and maintaining data pipelines that ingest data from sensors MES ERP CRM and enterprise databases.
Perform data cleaning validation and basic feature engineering to ensure high-quality consistent datasets for model development.
Follow data governance and security standards in all data handling.
Operational Support & Monitoring
Support live AI systems by monitoring performance running routine checks and helping resolve assigned incidents.
Track basic performance metrics and flag anomalies or opportunities for improvement to senior team members.
Learn and apply MLOps practices version control CI/CD automated testing and model monitoring as part of the delivery workflow.
Collaboration & Communication
Work closely with senior engineers data teams and function stakeholders to understand requirements and deliver assigned tasks.
Provide clear timely updates on task progress risks and blockers.
Governance Compliance & Ethical AI
Adhere to corporate data governance cybersecurity regulatory and ethical AI (Responsible AI / DPDP) guidelines in all work.
Continuous Learning & Capability Building
Actively build skills across the AI toolkit tools and internal platforms and share learnings with the team.
Contribute to reusable assets documentation and internal knowledge repositories.
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