ELP 2026 Participant

Aditya Birla Group

Fresher

Mumbai, India

Early Applicant

Posted 15 hours ago
Be among the first 10 applicants

Job Description

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.