๐ Job Overview
- ๐ข Company Name: Arcbizz Technologies
- ๐ผ Job Title: AI/ML Engineer
- ๐ Location: Mota Varachha and Adajan, Surat, Gujarat
- ๐ข Work Mode: On-site
- โณ Experience Required: 0โ3 years
- ๐ Qualification: Specific educational qualification not disclosed
- ๐ Employment Type: Full-time
- ๐จโ๐ป Eligible Candidates: Freshers and experienced professionals who meet the required skills
- ๐ง Key Technologies: Python, RAG, LangChain, AI workflows, and automation
- ๐ Location Preference: Local candidates preferred
- ๐ฉ Application Email: info@arcbizz.com
- ๐ Contact Number: 8758027188
- ๐ Job ID: Not specified
- ๐ฐ Salary: Not disclosed
๐ ๏ธ Key Responsibilities
The announcement does not provide a detailed responsibilities list. Based on the advertised skills, the following are relevant areas of work for an AI/ML Engineer:
- ๐ Python Development: Write readable, maintainable Python code for AI applications and supporting services.
- ๐ RAG Implementation: Work with techniques that retrieve relevant information from documents or knowledge sources to improve responses generated by language models.
- ๐ LangChain Integration: Explore and implement components for connecting language models, retrieval systems, prompts, and application logic.
- โ๏ธ AI Workflow Automation: Develop workflows that connect different AI components and reduce repetitive manual tasks.
- ๐งช Testing and Debugging: Evaluate application outputs, investigate errors, and improve the reliability of AI-powered solutions.
- ๐ค Team Communication: Explain technical approaches, discuss implementation challenges, and communicate progress clearly in English.
Note: These responsibilities are role-relevant expectations, not an official task list supplied by the employer.
๐ Why Is This Role a Good Opportunity?
This opening may suit aspiring AI engineers who want to build practical skills in a fast-developing technology area.
- ๐ Entry-Level Access: Freshers can apply if they demonstrate the required technical knowledge.
- ๐ง Relevant AI Technologies: RAG and LangChain are useful technologies for developing applications powered by large language models.
- ๐ Practical Python Application: Candidates can build on their programming knowledge while working toward AI engineering roles.
- โ๏ธ Workflow Experience: Understanding AI automation can help candidates explore intelligent assistants, document-processing systems, and other workflow-based applications.
- ๐ Local Employment Opportunity: Candidates based in or around Surat may find the on-site arrangement convenient.
Career growth and project assignments will depend on the employer’s requirements and the selected candidate’s responsibilities.
๐ง Skills Required
Candidates should focus on the skills explicitly mentioned in the announcement:
- ๐ Python: Strong programming fundamentals, functions, classes, data structures, exception handling, and debugging.
- ๐ Retrieval-Augmented Generation: Understanding document retrieval, chunking, embeddings, similarity search, and context-grounded generation.
- ๐ LangChain: Familiarity with its components for building applications that combine language models, prompts, and retrieval.
- โ๏ธ AI Workflows and Automation: Ability to understand how multiple steps and AI components work together.
- ๐ฃ๏ธ English Communication: Ability to explain technical concepts, document findings, and discuss solutions clearly.
Additional familiarity with vector databases, APIs, Git, and model evaluation may be helpful, but these are not confirmed mandatory requirements.
๐ค Expected Interview Rounds
The company’s actual selection process has not been disclosed. The following stages are indicative preparation suggestions, not confirmed interview rounds.
- ๐ Initial Screening: Prepare to discuss your background, interest in AI/ML, availability for on-site work, and relevant projects.
- ๐ Python Technical Round: Revise data structures, functions, object-oriented programming, exception handling, and common coding problems.
- ๐ AI/ML and RAG Discussion: Be ready to explain embeddings, document chunking, retrieval quality, hallucinations, and how RAG differs from model fine-tuning.
- ๐ LangChain Practical Assessment: Practise building a basic question-answering application using retrieved documents and a language model.
- ๐ค Communication or Final Discussion: Prepare to explain your project decisions, troubleshoot examples, and describe your individual contributions.
๐ Preparation Tips for This Specific Job
Improve your readiness by practising tasks aligned with the advertised technology stack:
- ๐ Strengthen Python: Solve problems involving dictionaries, lists, strings, file handling, functions, and classes. Practise writing clean code and explaining your approach.
- ๐ Build a RAG Project: Create a document-question-answering application that accepts PDF or text documents, splits them into chunks, generates embeddings, retrieves relevant passages, and produces answers grounded in those passages.
- ๐ Practise LangChain: Understand how prompts, retrievers, document loaders, language models, and output parsers fit together. Verify current library APIs when building projects.
- ๐งช Evaluate Responses: Test your application with relevant and irrelevant questions. Check retrieval accuracy, unsupported answers, response consistency, and source relevance.
- โ๏ธ Demonstrate Automation: Build a simple workflow that takes an input, retrieves relevant information, generates a response, and handles errors.
- ๐ฃ๏ธ Prepare Project Explanations: Practise describing the problem, architecture, technology choices, limitations, and improvements in clear English.
๐ Resume Tips
Make your resume relevant to AI engineering rather than listing every technology you have encountered.
- ๐ Highlight Keywords: Include Python, Retrieval-Augmented Generation (RAG), LangChain, AI workflows, and automation where they accurately reflect your skills.
- ๐ป Show Practical Projects: Describe a RAG chatbot, document-search application, or AI workflow you have personally developed.
- ๐ Demonstrate Contributions: Explain what you implemented, which technical challenges you solved, and how you evaluated results.
- ๐งฐ List Relevant Tools: Include embeddings, vector databases, APIs, Git, and other tools only if you have genuinely used them.
- ๐ Keep Formatting ATS-Friendly: Use clear headings, concise bullet points, readable fonts, and a straightforward layout.
Freshers can highlight academic projects, personal experiments, Git repositories, and relevant coursework instead of claiming professional experience they do not have.
๐ฉ How to Apply
Interested candidates can submit their CV using the contact details provided in the original hiring announcement:
- ๐ง Email: info@arcbizz.com
- ๐ Phone: 8758027188
- ๐ Subject Suggestion: Application for AI/ML Engineer โ Surat
Mention your relevant Python, RAG, and LangChain experience in your email. Freshers should briefly describe their practical projects and technical skills. Since the position is on-site, candidates should also indicate whether they can work from the Surat locations.
Verify the employer and role details before sharing personal information. The announcement does not specify a separate application URL.
๐ Final Thoughts
The AI/ML Engineer opportunity at Arcbizz Technologies is worth exploring for freshers and professionals with up to three years of experience who have practical knowledge of Python, RAG, LangChain, and AI workflow automation. Candidates who can demonstrate a working project and explain their technical decisions clearly may be better prepared for the selection process.
๐ Build your skills, showcase your projects, and apply through the contact details provided in the hiring announcement.
