Junior Generative AI Developer

We’re looking for a motivated Junior Generative AI Developer to join our newly launched IT Pod project. This is a hands-on individual contributor role where you’ll collaborate with senior engineers to design, implement, and optimize cutting-edge Generative AI solutions. You’ll work with technologies like LLMs (GPT-4, Claude, Gemini), diffusion models, and multimodal systems — all while following ethical AI practices.

1.Model Development & Fine-Tuning

• Assist in training and fine-tuning generative models (text, image, code) using PyTorch, TensorFlow, or JAX

• Implement RAG (Retrieval-Augmented Generation) pipelines and optimize prompts for specific domains

2. Tooling & Integration

• Build applications using LangChain, LlamaIndex, Hugging Face Transformers

• Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enterprise workflows

3. Prompt Engineering

• Design and test advanced prompting strategies (few-shot, chain-of-thought, ReAct)

• Create reusable prompt templates for workflows like customer support, code generation, and content moderation

4. Evaluation & Optimization

• Develop metrics for hallucination reduction, output consistency, and safety alignment

• Optimize inference costs using quantization, distillation, or speculative decoding

5. Collaboration

• Work with cross-functional teams (product, data, UX) to deploy AI solutions

• Document processes and contribute to knowledge-sharing sessions

Qualifications

Education:

• Bachelor’s or Master’s in Computer Science, Data Science, or related field

Technical Skills:

• Proficiency in Python and familiarity with PyTorch or TensorFlow

• Basic understanding of NLP and neural architectures (Transformers, GANs)

• Experience with cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML)

• Familiarity with prompt engineering tools (LangChain, DSPy, Guidance, LMQL)

• Experience with deployment tools (FastAPI, Docker, MLflow)

AI/GenAI Exposure:Experience with at least two of the following:

• Hands-on projects with LLMs or diffusion models

• Vector databases (Pinecone, Milvus) and orchestration tools

• Fine-tuning LLMs (Llama 2, Mistral) using LoRA, QLoRA, RLHF

• Building RAG pipelines with embedding models (BERT, OpenAI)

• Developing applications with Stable Diffusion, DALL·E

• NLP projects using spaCy or NLTK

Soft Skills:

• Strong problem-solving mindset and curiosity about emerging AI trends

• Ability to explain technical concepts to non-technical stakeholders

Preferred Qualifications

• Certifications:

  • Microsoft Certified: Azure AI Engineer Associate

  • Google Cloud Professional Machine Learning Engineer