Artificial Intelligence Intern

Company Description

Vyva Consulting Inc. is a trusted partner in Sales Performance Management (SPM) and Incentive Compensation Management (ICM), specializing in delivering top-tier software consulting solutions. We help organizations optimize their sales operations, boost revenue, and maximize value. Our seasoned experts work with leading products such as Xactly, Varicent, and SPIFF, offering comprehensive implementation and post-implementation services. We focus on enhancing sales compensation strategies to drive business success.

Role Description

This is a full-time, on-site role for an Artificial Intelligence Intern, located in Hyderabad. We are seeking a motivated AI Engineer Intern to join our team and contribute to cutting-edge AI/ML projects. This internship offers hands-on experience with large language models, generative AI, and modern AI frameworks while working on real-world applications that impact our business objectives.

• LLM Integration & Development:

  Build and prototype LLM-powered features using frameworks like LangChain, OpenAI SDK, or similar tools for content automation and intelligent workflows

• RAG System Implementation:

  Design and optimize Retrieval-Augmented Generation systems including document ingestion, chunking strategies, embedding generation, and vector database integration

• Data Pipeline Development:

  Create robust data pipelines for AI/ML workflows, including data collection, cleaning, preprocessing, and annotation of large datasets

• Model Experimentation:

  Conduct experiments to evaluate, fine-tune, and optimize AI models for accuracy, performance, and scalability across different use cases

• Vector Database Operations:

  Implement similarity search solutions using vector databases (FAISS, Pinecone, Chroma) for intelligent Q&A, content recommendation, and context-aware responses

• Prompt Engineering:

  Experiment with advanced prompt engineering techniques to optimize outputs from generative models and ensure content quality

• Research & Innovation:

  Stay current with latest AI/ML advancements, research new architectures and techniques, and build proof-of-concept implementations

• Deploy AI micro services and agents using containerization (Docker) and orchestration tools
• Collaborate with cross-functional teams (product, design, engineering) to align AI features with business requirements
• Create comprehensive documentation including system diagrams, API specifications, and implementation guides
• Analyze model performance metrics, document findings, and propose data-driven improvements
• Participate in code reviews and contribute to best practices for AI/ML development
  

• Currently pursuing or recently completed Bachelor's/Master's degree in Computer Science, Data Science, AI/ML, or related field
• 6+ months of hands-on experience with AI/ML projects (academic, personal, or professional)
• Demonstrable portfolio of AI/ML projects via GitHub repositories, Jupyter notebooks, or deployed applications

• Programming:

  Strong Python proficiency with experience in AI/ML libraries (NumPy, Pandas, Scikit-learn)

• LLM Experience:

  Practical experience with large language models (OpenAI GPT, Claude, open-source models) including API integration and fine-tuning

• AI Frameworks:

  Familiarity with at least one: LangChain, OpenAI Agents SDK, AutoGen, or similar agentic AI frameworks

• RAG Architecture:

  Understanding of RAG system components and prior implementation experience (even in academic projects)

• Vector Databases:

  Experience with vector similarity search using FAISS, Chroma, Pinecone, or similar tools

• Deep Learning:

  Familiarity with PyTorch or TensorFlow for model development and fine-tuning

To effectively evaluate candidates, we will assess:

1. Portfolio Quality:

   Live demos or well-documented projects showing AI/ML implementation

2. Technical Depth:

   Ability to explain RAG architecture, vector embeddings, and LLM fine-tuning concepts

3. Problem-Solving:

   Approach to handling real-world AI challenges like hallucination, context management, and model evaluation

4. Code Quality:

   Clean, documented Python code with proper version control practices

• Full-Stack Development:

  Experience building web applications with AI/ML backends

• Data Analytics:

  Proficiency in data manipulation (Pandas/SQL), visualization (Matplotlib/Seaborn), and statistical analysis

• MLOps/DevOps:

  Experience with Docker, Kubernetes, MLflow, or CI/CD pipelines for ML models

• Cloud Platforms:

  Familiarity with AWS, Azure, or GCP AI/ML services

• Databases:

  Experience with both SQL (PostgreSQL) and NoSQL (Elasticsearch, MongoDB) databases

• Analytical Mindset:

  Strong problem-solving skills with attention to detail in model outputs and data quality

• Communication:

  Ability to explain complex AI concepts clearly to both technical and non-technical stakeholders

• Collaboration:

  Proven ability to work effectively in cross-functional teams

• Learning Agility:

  Demonstrated ability to quickly adapt to new technologies and frameworks

• Initiative:

  Self-motivated with ability to work independently and drive projects forward

• Mentorship:

  Work directly with senior AI engineers and receive structured guidance

• Real Impact:

  Contribute to production AI systems used by real customers

• Learning Opportunities:

  Access to latest AI tools, frameworks, and industry conferences

• Full-Time Conversion:

  Potential for full-time offer based on performance and business needs

• Employee-First Culture:

  Flexible work arrangements with emphasis on results

• Innovation Focus:

  Opportunity to work on cutting-edge AI applications

• Collaborative Team:

  Supportive environment that values diverse perspectives and ideas

• Competitive Compensation:

  Market-competitive internship stipend

Please include the following in your application:

1. GitHub Repository:

   Link to your best AI/ML projects with detailed README files

2. Project Demo:

   Video walkthrough or live demo of your most impressive AI application

3. Technical Blog/Documentation:

   Any technical writing about AI/ML concepts or implementations

4. Resume:

   Highlighting relevant coursework, projects, and any AI/ML experience

Qualified candidates will complete a technical assessment covering:

• Python programming and AI/ML libraries
• LLM integration and prompt engineering
• RAG system design and implementation
• Vector database operations and similarity search
• Model evaluation and optimization techniques

Ready to shape the future of AI?