Trainee Software Engineer - Ai

The Role

As a Trainee Software Engineer - AI, you will work as part of the AI and engineering team to build, test,

and improve AI-enabled applications and components.

You will be expected to take ownership of assigned deliverables, write production-quality code, work

with modern AI development tools, and demonstrate measurable progress throughout the training

period.

AI-assisted development is an integral part of how our engineering teams work. Candidates are

expected to become productive early with AI-assisted development tools while maintaining strong

engineering judgment, code quality, and an understanding of the systems they build.

Key Responsibilities

• Build AI-Enabled Applications: Develop and ship assigned components of AI-powered applications using Python and relevant frameworks, APIs, and tools.
• Develop LLM and Generative AI Solutions: Build and evaluate practical applications involving Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, embeddings,

vector databases, and related technologies where applicable.

• Prepare and Work with Data: Own assigned data preparation and processing tasks, including cleaning, transformation, analysis, and preparation of datasets required for AI and software applications.
• Build, Test, and Improve Solutions: Write clean and maintainable code, test assigned components,debug issues, evaluate outputs, and improve the performance and reliability of the solutions you

own.

• Research and Convert Ideas into Working Prototypes: Evaluate relevant technologies,frameworks, and approaches and translate useful findings into working prototypes or technical implementations.
• Collaborate and Deliver: Work closely with AI Engineers, Software Engineers, and technical leads to understand requirements, communicate progress, participate in reviews, and deliver assigned

work within agreed timelines.

Skill Sets & Qualifications

Mandatory Requirements:

• B.Tech / M.Tech / M.Sc in Computer Science, Artificial Intelligence, Data Science, Statistics, or a related technical discipline.
• Preferred CGPA of 8.5 or above. Candidates with exceptional technical achievements may also be considered through alternate qualifying routes such as strong projects, recognised hackathon

achievements, research work, or significant open-source contributions.

• Candidates must demonstrate practical technical experience through at least one substantial project, GitHub repository, live application, hackathon project, research implementation, or

technical portfolio. Candidates should be able to clearly explain their individual contribution and technical implementation.

• Strong programming knowledge in Python.
• Good understanding of Data Structures, Algorithms, Object-Oriented Programming, and software engineering fundamentals.
• Good understanding of fundamental Artificial Intelligence and Machine Learning concepts,including data preprocessing and model evaluation.
• Hands-on experience or practical project exposure in Generative AI and Large Language Models (LLMs) is mandatory.
• Practical understanding of one or more Generative AI concepts such as Prompt Engineering, RAG,embeddings, vector databases, transformers, or AI Agents.
• Experience working with relevant Generative AI tools, frameworks, or APIs such as LangChain,LangGraph, Hugging Face, OpenAI APIs, or similar technologies.
• Candidates should be comfortable using AI-assisted development tools for coding, debugging,research, and problem-solving while maintaining a clear understanding of the solutions they develop.
• Strong analytical and problem-solving abilities, along with the ability to learn quickly, take ownership of assigned tasks, and deliver quality work.

Good to Have

• Familiarity with SQL, databases, APIs, and backend development concepts.
• Knowledge of mathematics relevant to AI/ML, including linear algebra, probability, and statistics.
• Exposure to frameworks such as FastAPI, PyTorch, or TensorFlow.
• Knowledge or exposure to areas such as NLP, Computer Vision, or multimodal AI.
• Previous participation in recognised hackathons, research projects, internships, or significant open-source contributions.
• Good communication and teamwork skills.