As a Product Intern - Machine Learning & Gen-AI at Enverus, you will have the opportunity to work on cutting-edge AI initiatives, develop your technical skills, and contribute to impactful projects. This 6-month internship is perfect for freshers and early-career professionals looking to gain hands-on experience in machine learning and AI.
Enverus is a leading energy SaaS company that provides data-driven solutions to the energy industry. With a strong presence in India, Enverus is committed to innovation and excellence, making it an exciting place to work for freshers and experienced professionals alike.
Why required: Python is a fundamental skill required for machine learning and AI development. You will use Python to build models, experiment with generative AI techniques, and contribute to intelligent product features.
How recruiters evaluate: The recruiter will evaluate your Python skills by assessing your ability to write clean, efficient, and well-documented code.
Why required: Machine learning is a critical component of AI-driven solutions. You will use machine learning concepts to develop models, experiment with generative AI techniques, and contribute to intelligent product features.
How recruiters evaluate: The recruiter will evaluate your machine learning skills by assessing your understanding of core concepts, such as supervised and unsupervised learning, regression, and classification.
Why required: AI is a key aspect of the role, and you will use AI techniques to develop models, experiment with generative AI techniques, and contribute to intelligent product features.
How recruiters evaluate: The recruiter will evaluate your AI skills by assessing your understanding of AI concepts, such as natural language processing, computer vision, and robotics.
Why required: SQL is a fundamental skill required for data analysis and manipulation. You will use SQL to clean, prepare, and analyze datasets.
How recruiters evaluate: The recruiter will evaluate your SQL skills by assessing your ability to write efficient and effective queries.
Why required: Data analysis is a critical component of the role, and you will use data analysis techniques to clean, prepare, and analyze datasets.
How recruiters evaluate: The recruiter will evaluate your data analysis skills by assessing your ability to extract insights from data and communicate findings effectively.
Why required: Git is a fundamental skill required for version control and collaboration. You will use Git to manage code repositories and collaborate with team members.
How recruiters evaluate: The recruiter will evaluate your Git skills by assessing your ability to use Git commands and manage code repositories effectively.
Why required: Problem-solving is a critical component of the role, and you will use problem-solving skills to develop models, experiment with generative AI techniques, and contribute to intelligent product features.
How recruiters evaluate: The recruiter will evaluate your problem-solving skills by assessing your ability to break down complex problems into manageable components and develop effective solutions.
Why required: Analytical thinking is a critical component of the role, and you will use analytical thinking skills to develop models, experiment with generative AI techniques, and contribute to intelligent product features.
How recruiters evaluate: The recruiter will evaluate your analytical thinking skills by assessing your ability to analyze complex data and develop effective solutions.
Why required: LLM frameworks are a key aspect of the role, and you will use LLM frameworks to develop models and experiment with generative AI techniques.
How recruiters evaluate: The recruiter will evaluate your LLM framework skills by assessing your understanding of LLM concepts and your ability to use LLM frameworks effectively.
Why required: Prompt engineering is a critical component of the role, and you will use prompt engineering skills to develop models and experiment with generative AI techniques.
How recruiters evaluate: The recruiter will evaluate your prompt engineering skills by assessing your ability to design effective prompts and develop models that respond to those prompts.
Why required: RAG is a key aspect of the role, and you will use RAG to develop models and experiment with generative AI techniques.
How recruiters evaluate: The recruiter will evaluate your RAG skills by assessing your understanding of RAG concepts and your ability to use RAG effectively.
Why required: Fine-tuning language models is a critical component of the role, and you will use fine-tuning language models to develop models and experiment with generative AI techniques.
How recruiters evaluate: The recruiter will evaluate your fine-tuning language model skills by assessing your ability to fine-tune language models effectively and develop models that respond to those prompts.
Why required: Custom model training is a key aspect of the role, and you will use custom model training to develop models and experiment with generative AI techniques.
How recruiters evaluate: The recruiter will evaluate your custom model training skills by assessing your ability to train custom models effectively and develop models that respond to those prompts.
Freshers with a strong background in machine learning and AI are encouraged to apply.
Experienced professionals with a background in machine learning and AI can also apply, but the role is geared towards freshers and early-career professionals.
Graduates with a degree in computer science, mathematics, or a related field are eligible to apply.
B.Tech graduates with a specialization in computer science or a related field are eligible to apply.
MCA graduates with a specialization in computer science or a related field are eligible to apply.
Diploma holders with a specialization in computer science or a related field are eligible to apply.
The salary range for this role in the industry is ₹6-12 lakhs per annum.
The salary range for freshers in this role is ₹6-8 lakhs per annum.
The salary range for experienced professionals in this role is ₹10-15 lakhs per annum.
The salary growth for this role is expected to be 10-15% per annum.
A machine learning engineer is responsible for developing and deploying machine learning models.
An AI researcher is responsible for researching and developing new AI techniques and models.
A data scientist is responsible for analyzing and interpreting complex data to gain insights and make informed decisions.
This course provides a comprehensive introduction to machine learning with Python.
This course provides a broad introduction to AI and its applications.
This course provides a comprehensive introduction to LLM frameworks.
To succeed in this role, you need to have a strong background in machine learning and AI, as well as excellent problem-solving skills. You should also be able to communicate complex technical concepts effectively to both technical and non-technical stakeholders.
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Product Intern - Machine Learning & Gen-AI - 26294
Product Development Bangalore, India
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Description
About the Role
As a Machine Learning & Generative AI Intern, you will play a hands-on role in developing and enhancing AI‑driven solutions that address real business challenges. You will collaborate with experienced ML engineers and data scientists to build models, experiment with cutting‑edge generative AI techniques, and contribute to intelligent product features. This internship offers an opportunity to deepen your technical skills while working on impactful, production‑focused AI initiatives. This is a 6-month internship that requires working from our Bangalore office.
Key Responsibilities
Assist in developing machine learning and AI-based solutions
Work on generative AI use cases such as text analysis, summarization, or chatbot-style applications
Help clean, prepare, and analyze datasets
Support model testing, evaluation, and performance improvement
Contribute to experiments and proof-of-concepts of Ideas
Communicate findings clearly with both technical and business team
Required Skills
Understanding of core machine learning concepts
Strong Python & ML/AI model development skills
Knowledge of SQL and data analysis
Introduction agentic workflows, prompt engineering, retrieval-augmented generation (RAG), and building AI agents using LLM frameworks
Working knowledge of Git
Strong problem-solving and analytical thinking
Preferred Qualifications
Prior Internships or Academic/personal projects in ML or AI
Experience with fine‑tuning language models (LLMs) or custom model training is a strong plus
