Role: Senior Generative AI & Full Stack Engineer

Experience: 5+yrs

Shifts: 8am- 5pm IST. 

Work Location: Pune

1. List of Skills
Category Skills
Strong Expertise- Python Programming
- Machine Learning & Deep Learning
- Natural Language Processing (NLP)
- Generative AI Solution Development
- Full Stack Development (Frontend + Backend)
- Model Fine-Tuning & Optimization
- MLOps & CI/CD Pipelines
- Data Science & Statistics
Basic Proficiency- Conversational AI (Chatbots, LangChain, AutoGen)
- Data Engineering & Processing (Spark, Pandas, NumPy, PyTorch, TensorFlow)
- Cloud AI Platforms (AWS, Azure)
- API Development & Integration
Knowledge Only- Open-source AI contributions
- Software Design Principles
- AI Ethics & Bias Mitigation
- Agile/Scrum Development Practices
 
2. Primary Skills

1. Generative AI & Large Language Model (LLM) based Solution Development
   Design, develop, and fine-tune cutting-edge generative AI models, including open-source LLMs, for real-world, scalable applications.
2. Natural Language Processing (NLP)
   Apply NLP techniques for tasks like text generation, summarization, translation, entity recognition, and sentiment analysis using frameworks like Hugging Face, spaCy, and OpenAI GPT.
3. Full Stack Development
   Design and implement scalable end-to-end applications integrating AI models using modern front-end (React, Angular, etc.) and back-end (FastAPI, Django, Node.js) technologies.
4. Python Programming for AI & Web Applications
   Leverage Python for both AI algorithm development and backend services, ensuring seamless integration between machine learning models and application logic.
5. Machine Learning & Deep Learning
   Apply supervised, unsupervised, and reinforcement learning techniques using libraries such as Scikit-learn, TensorFlow, and PyTorch.
6. Model Fine-Tuning & Optimization
   Customize pre-trained models with domain-specific data, improving accuracy and efficiency in inference workflows.
7. ML Ops & Production-Ready AI Deployment
   Build and manage robust ML Ops pipelines including Docker, Kubernetes, and CI/CD for continuous deployment, monitoring, and model management.

 
3. Secondary Skills

1. Conversational AI & Chatbot Frameworks
   Develop AI-powered virtual assistants using LangChain, AutoGen, or similar libraries to meet enterprise conversational requirements.
2. Cloud AI Platform Integration
   Deploy and scale applications using cloud platforms such as Azure, or AWS for secure, high-performance model hosting.
3. Data Engineering & Preprocessing
   Collaborate with data engineers to process and manage large datasets with tools like Apache Spark, Pandas, and SQL for training and evaluation.
4. API Development & Integration
   Build RESTful APIs to expose AI capabilities and enable seamless integration with business systems and UI components.
5. AI Research, Innovation & Open-Source Contribution
   Stay updated with the latest trends in generative AI and contribute to open-source AI projects to stay ahead in innovation and design.
6. AI Ethics & Bias Mitigation
   Awareness of ethical AI design and practices to ensure responsible AI deployments and fair model behaviour in production environments.