Planso Junior AI Engineer: Eligibility, Skills, Responsibilities and Application Details
Planso is hiring for a Junior AI Engineer position in Bengaluru. This is a full-time opportunity for candidates interested in applied artificial intelligence, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), document intelligence and AI agent workflows.
Planso is an early-stage startup building AI-powered decision-intelligence products for the global architecture, engineering and construction industry. The company works with enterprise data, documents and domain-specific reasoning to develop AI solutions for complex real-world problems.
The Junior AI Engineer will work on developing and evaluating LLM-powered capabilities, building document-processing pipelines, experimenting with retrieval and agentic workflows, and taking AI prototypes into production alongside engineers and domain specialists.
Planso Junior AI Engineer – Job Overview
| Particular | Details |
|---|---|
| Company | Planso |
| Job Role | Junior AI Engineer |
| Job Type | Full Time |
| Location | Bengaluru, Karnataka |
| Work Arrangement | Not specified in the supplied listing |
| Experience Required | Not specifically stated |
| Key Programming Language | Python |
| Core Areas | AI, LLMs, NLP, Information Retrieval, RAG, AI Agents |
| AI Technologies | LLM APIs, Embeddings, RAG, Reranking |
| Preferred Frameworks | PyTorch, Hugging Face, LangChain, LangGraph, LlamaIndex |
| API Framework | FastAPI |
| Visa Sponsorship | Not Available |
| Relocation | Not Allowed |
| Application Materials | Résumé, GitHub or portfolio, and short AI project note |
| Application Deadline | Not specified |
About Planso
Planso develops AI-powered decision-intelligence products for the architecture, engineering and construction sector.
The company works on problems involving:
- Enterprise data
- Documents
- Domain-specific reasoning
- Information extraction
- Retrieval
- AI-powered decision support
As an early-stage startup, Planso describes its environment as one where team members can take significant ownership while working closely with the founding and engineering teams.
About the Junior AI Engineer Role
The Junior AI Engineer will contribute to the development of LLM-powered product capabilities and applied AI systems.
The role combines experimentation with practical product development. Candidates will work with technologies and techniques such as embeddings, reranking, RAG and agentic workflows, while also creating evaluation datasets and analysing model failures.
The position is therefore suited to candidates who are interested in applying AI to structured and unstructured enterprise data rather than focusing only on theoretical machine learning.
Key Responsibilities
Build LLM-Powered Features
The selected candidate will help build and evaluate product capabilities powered by Large Language Models.
This includes experimenting with AI techniques and assessing whether the resulting capabilities are accurate, relevant and reliable.
Develop Document Understanding Pipelines
The role involves developing pipelines for:
- Document understanding
- Information extraction
- Information retrieval
This work is particularly relevant to Planso’s focus on enterprise documents and complex industry-specific information.
Experiment With RAG and Agentic Workflows
Candidates will experiment with modern AI application techniques, including:
- Embeddings
- Reranking
- Retrieval-Augmented Generation (RAG)
- Agentic workflows
The goal is to develop AI capabilities that can work effectively with enterprise information and domain-specific requirements.
Create Evaluation Datasets
The Junior AI Engineer will create datasets for evaluating AI systems and measure factors such as:
- Accuracy
- Relevance
- Reliability
An understanding of evaluation metrics is therefore important for the role.
Analyse and Improve AI Systems
The position involves investigating failure cases and using those findings to improve:
- Prompts
- Models
- Guardrails
This requires a methodical approach to identifying why an AI system produces an undesirable result and determining how it can be improved.
Take Prototypes Into Production
The selected engineer will work with engineers and domain specialists to move AI prototypes toward production-ready systems.
Document Experiments and Technical Decisions
Clear documentation is part of the role. Candidates will be expected to maintain records of experiments and technical decisions.
Required Skills and Qualifications
Python and Programming Fundamentals
Strong Python skills and programming fundamentals are required.
Candidates should be comfortable writing code and applying sound programming practices when developing AI systems.
Machine Learning and NLP
The job description seeks candidates with an understanding of at least relevant areas such as:
- Machine learning
- Natural Language Processing (NLP)
- Information retrieval
LLM APIs, Embeddings and RAG
Candidates should be familiar with:
- LLM APIs
- Embeddings
- Retrieval-Augmented Generation
These technologies form an important part of the position.
Structured and Unstructured Data
The role requires the ability to work with both structured and unstructured data.
This is particularly relevant because Planso’s products involve enterprise information and document-based workflows.
Evaluation Metrics
Candidates should understand evaluation metrics such as:
- Precision
- Recall
These metrics are useful when assessing the quality of information retrieval and related AI systems.
Problem-Solving and Ownership
The company is looking for candidates who demonstrate:
- Strong problem-solving ability
- Ownership
- Curiosity
These qualities are particularly relevant to an early-stage startup environment where engineers may work on open-ended problems.
Good-to-Have Skills
The following technologies and experiences can provide an advantage:
AI and Machine Learning Frameworks
- PyTorch
- Hugging Face
LLM and AI Application Frameworks
- LangChain
- LangGraph
- LlamaIndex
Data Infrastructure
Experience with:
- Vector databases
- Knowledge graphs
- Document-processing systems
can also be beneficial.
API Development
Experience building APIs using FastAPI is listed as a good-to-have skill.
Projects and Internships
Relevant internships, academic projects or personal AI projects can strengthen a candidate’s application.
Application Requirements
Applicants are asked to provide:
- A résumé
- GitHub profile or portfolio
- A short note about an AI project they have built
- One challenge encountered during the project
- An explanation of how that challenge was addressed
Candidates should use this project note to demonstrate practical problem-solving rather than simply describing the project’s features.
Who Can Apply?
The supplied job description does not specify a particular:
- Degree
- Graduation year
- Minimum CGPA
- Academic percentage
- Years of professional experience
Instead, the requirements focus on technical ability and practical exposure to AI technologies.
Candidates with relevant academic projects, personal projects or internships may therefore find the role relevant, provided they can demonstrate the required skills.
Work Location and Relocation
The position is based in Bengaluru.
The listing states that relocation is not allowed.
The supplied job description does not specify whether the position is remote, hybrid or fully office-based.
Salary and Benefits
The supplied listing does not mention a specific salary or compensation figure.
The benefits highlighted by Planso include:
- Experience building production-grade applied AI systems
- Exposure to enterprise AI
- Experience working with complex real-world data
- Close collaboration with founding and engineering teams
- Significant ownership
- A fast learning environment
Why Consider This AI Engineering Role?
The role provides exposure to several areas of applied AI development rather than being limited to a single technology.
The selected candidate can work on:
- LLM-powered products
- Document intelligence
- Information extraction
- Information retrieval
- RAG
- AI agents
- Evaluation systems
- AI reliability
- Enterprise data
- Production AI systems
The position also offers an opportunity to work closely with founding and engineering teams in an early-stage startup environment.
Application Tips
Candidates preparing an application can focus on demonstrating hands-on AI development experience.
Showcase a Practical AI Project
Choose a project that demonstrates your ability to build something using LLMs, NLP, retrieval, document processing or another relevant AI technology.
Explain a Challenge
The application specifically asks for a challenge encountered during an AI project and how it was addressed.
Use this opportunity to demonstrate your debugging, experimentation and problem-solving approach.
Highlight RAG or LLM Experience
If you have worked with RAG, embeddings, vector databases, LLM APIs or AI agents, make those experiences easy to identify.
Include GitHub or Portfolio Work
A relevant GitHub repository or portfolio can provide evidence of practical programming and AI engineering skills.
Demonstrate Evaluation Thinking
Because the role involves evaluating AI systems, highlight projects where you measured accuracy, relevance, precision, recall or another meaningful evaluation metric.
Selection Process
The provided job description does not specify the recruitment or interview process.
Candidates should therefore not assume a particular number of technical interviews, coding tests or assessment rounds.
Important Details for Applicants
- Planso is hiring for a Junior AI Engineer.
- The position is full time.
- The job is based in Bengaluru.
- Relocation is not allowed.
- Visa sponsorship is not available.
- Strong Python and programming fundamentals are required.
- Understanding of ML, NLP or information retrieval is expected.
- Familiarity with LLM APIs, embeddings and RAG is required.
- Candidates should be comfortable working with structured and unstructured data.
- Precision and recall are among the evaluation metrics mentioned.
- PyTorch, Hugging Face, LangChain, LangGraph and LlamaIndex are good-to-have technologies.
- Vector databases, knowledge graphs and document-processing experience can be beneficial.
- FastAPI experience is listed as an advantage.
- Relevant internships, academic projects and personal projects are accepted as useful experience.
- Applicants should provide a résumé, GitHub or portfolio and a short AI project note.
- Salary and application deadline are not specified in the supplied listing.
Frequently Asked Questions
What is the Planso Junior AI Engineer role?
It is a full-time AI engineering position focused on LLM-powered products, document understanding, information retrieval, RAG, AI agents and enterprise AI systems.
Where is the Planso Junior AI Engineer job located?
The position is based in Bengaluru.
Is relocation available?
No. The listing states that relocation is not allowed.
Is visa sponsorship available?
No. The listing states that visa sponsorship is not available.
What programming language is required?
Strong Python and programming fundamentals are required.
What AI skills are required?
Candidates should understand machine learning, NLP or information retrieval and be familiar with LLM APIs, embeddings and RAG.
Which AI frameworks are preferred?
The good-to-have technologies include PyTorch, Hugging Face, LangChain, LangGraph and LlamaIndex.
Is FastAPI experience required?
FastAPI experience is listed as a good-to-have skill rather than a core requirement.
Can students or candidates with project experience apply?
The listing does not specify a minimum professional experience requirement and mentions relevant internships, academic projects or personal projects as useful experience.
What should applicants submit?
Applicants should submit a résumé, GitHub or portfolio, along with a short note describing an AI project, a challenge encountered and how it was solved.
What is the salary for the Junior AI Engineer role?
The supplied job description does not specify a salary or compensation amount.
What is the application deadline?
The application deadline is not specified in the provided information.
Conclusion
The Planso Junior AI Engineer opportunity in Bengaluru is focused on applied AI development for enterprise use cases in the architecture, engineering and construction industry. The role combines Python programming, LLMs, RAG, document understanding, information retrieval, evaluation and AI agent workflows.
Candidates can strengthen their applications by demonstrating practical AI projects, strong programming fundamentals and the ability to investigate and solve technical challenges. Familiarity with technologies such as PyTorch, Hugging Face, LangChain, LangGraph, LlamaIndex, vector databases and FastAPI can provide an additional advantage.
Since the supplied listing does not specify salary, application deadline, selection stages, degree requirements or a minimum professional experience requirement, applicants should verify those details through the current recruitment process.