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: Write the 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 transparent, 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.

📌 ELP 2026 Participant (Mumbai)
🏢 Financial services
📍 Mumbai