Ksolves India Limited is hiring AI/ML Engineers with 1–3 years of experience for opportunities in Pune, Indore and Noida. This role is particularly relevant for professionals who have hands-on experience with Python, Machine Learning, Deep Learning, Generative AI, LLMs, RAG, LangChain, LangGraph and NLP and want to build practical AI-powered solutions.
🏢 About Ksolves India Limited
Ksolves India Limited is an AI-first software development company providing technology and digital transformation solutions across areas including AI/ML, Big Data, Salesforce, Odoo and enterprise software development. Ksolves says it has more than 600 AI-certified professionals and serves clients globally.
In April 2026, Ksolves announced its transition to an AI-first company, stating that AI training and certification had been extended across its workforce and that AI would be integrated into how it develops and delivers enterprise technology.
For candidates interested in GenAI, this is especially relevant because the company is actively positioning AI and Machine Learning as a core technology capability. Ksolves also reports experience delivering AI/ML solutions across enterprise use cases.
📌 Job Overview
- 🏢 Company: Ksolves India Limited
- 💼 Role: AI/ML Engineer
- 🎯 Experience: 1–3 Years
- 📍 Locations: Pune / Indore / Noida
- 🐍 Primary Language: Python
- 🤖 Core Areas: Machine Learning, Deep Learning, NLP
- 🧠 GenAI: Generative AI, LLM, RAG
- 🔗 Frameworks: LangGraph, LangChain or similar
- 🗣️ Important: Strong communication skills required
- 📩 CV: mohit.kumar@ksolves.com
💼 What Will You Work On?
As an AI/ML Engineer, your work can involve developing, training and integrating machine-learning models and building AI-powered applications. The JD’s emphasis on LLMs, RAG and LangChain/LangGraph indicates that candidates should be comfortable moving beyond traditional ML into modern GenAI application development.
Typical responsibilities may include preparing datasets, developing ML/DL models, experimenting with NLP techniques, integrating LLM APIs, designing retrieval pipelines, creating prompts and building AI workflows. You should also understand how to evaluate model output and improve reliability rather than treating an LLM response as automatically correct.
🌟 Why This Role Is Good for Experienced Professionals
For professionals with 1–3 years of experience, this position can provide exposure to both conventional AI/ML and newer GenAI engineering. Instead of focusing exclusively on model training, candidates can demonstrate skills across Python development, ML pipelines, NLP, LLM applications, RAG architectures and AI frameworks.
The combination is useful for candidates building an AI engineering profile because it connects foundational ML knowledge with practical enterprise-oriented GenAI development. Ksolves’ current positioning as an AI-first organisation also makes AI a central part of its technology direction.
🛠️ Skills Required
🔹 Core AI/ML
- Strong Python programming and object-oriented programming
- Machine Learning algorithms and model evaluation
- Deep Learning fundamentals
- NLP concepts including tokenisation, embeddings and text classification
- NumPy, Pandas and scikit-learn
🔹 Generative AI
- LLM fundamentals and transformer concepts
- Prompt engineering
- Embeddings and vector databases
- RAG architecture and retrieval strategies
- LLM evaluation and hallucination mitigation
🔹 AI Frameworks
- LangChain
- LangGraph
- Hugging Face or equivalent ecosystem
- REST APIs and model/API integration
- Git and basic deployment practices
🔹 Professional Skills
- Problem solving and analytical thinking
- Clear technical communication
- Ability to explain AI concepts to non-specialists
- Collaboration with software and product teams
🎯 Expected Ksolves Interview Rounds
- Round 1 – Screening/Assessment: Resume discussion, Python fundamentals, basic aptitude or technical MCQs and coding.
- Round 2 – Technical AI/ML: ML algorithms, Python, statistics, NLP, deep learning and project-based questions.
- Round 3 – GenAI/Project Deep Dive: LLMs, RAG, embeddings, LangChain/LangGraph, architecture and troubleshooting of your projects.
- Round 4 – HR/Communication: Communication skills, project ownership, career goals, availability and behavioural questions.
💰 Expected Salary Range
Public salary data should be treated as indicative rather than an official offer band. AmbitionBox’s available Ksolves data lists AI/ML Engineer salaries around ₹9–18 LPA, based on reported salary submissions for the designation.
Actual compensation for this 1–3 year role can vary based on experience, technical depth, location, interview performance and the specific project/team. Candidates should confirm the applicable package directly with the recruiter.
📚 Preparation Tips
- Build one complete RAG project: Explain document ingestion, chunking, embeddings, vector search, retrieval and LLM generation.
- Practise LangChain/LangGraph: Be able to explain chains, agents, tools, memory/state and workflow orchestration.
- Know Python deeply: Prepare functions, classes, decorators, generators, exception handling, data structures and API integration.
- Revise ML fundamentals: Classification, regression, clustering, overfitting, feature engineering, cross-validation and evaluation metrics.
- Prepare NLP concepts: Transformers, attention, embeddings, tokenisation and semantic similarity.
- Understand GenAI limitations: Be ready to discuss hallucinations, prompt injection, irrelevant retrieval, latency and evaluation.
- Practise communication: Explain one complex AI project in two minutes without relying on jargon.
📝 Resume Tips
- Put Python + AI/ML + GenAI + LLM + RAG near the top of your technical skills.
- Mention actual frameworks such as LangChain or LangGraph only if you have hands-on experience.
- Describe projects using measurable outcomes: accuracy, latency, retrieval quality, dataset size or response improvement.
- Add a dedicated GenAI Projects section if you have built RAG chatbots, AI agents or LLM applications.
- Show the complete architecture rather than simply writing “worked on GenAI.”
- Highlight NLP, deep learning and model-development experience separately.
- Include Git, APIs, vector databases and deployment/cloud exposure where applicable.
- Demonstrate communication through concise project descriptions that explain the problem → approach → technology → result.
🚀 How to Apply
📩mohit.kumar@ksolves.com
📩 Email your updated resume to: mohit.kumar@ksolves.com
📍 Mention your preferred location — Pune, Indore or Noida — in the email or resume.
