Role Overview

We seek a motivated Junior Generative AI Developer to design, implement, and optimize cutting-edge generative AI solutions. You’ll work closely with senior engineers to build applications leveraging LLMs (e.g., GPT-4, Claude, Gemini), diffusion models, and multimodal systems while adhering to ethical AI practices. This will be a hands-on individual contributor role.

Key Responsibilities

1. Model Development & Fine-Tuning

- Assist in developing, training, and fine-tuning generative models (text, image, code) using frameworks like PyTorch, TensorFlow, or JAX.
- Implement RAG (Retrieval-Augmented Generation) pipelines and optimize prompts for specific domains.

1. Tooling & Integration

- Build applications using tools like LangChain, LlamaIndex, or Hugging Face Transformers.
- Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enterprise workflows.

1. Prompt Engineering

- Design and test advanced prompting strategies (e.g., few-shot learning, chain-of-thought, ReAct frameworks) for domain-specific tasks (legal, healthcare, finance).
- Create reusable prompt templates for common workflows (customer support, code generation, content moderation).

1. Evaluation & Optimization

- Develop metrics for hallucination reduction, output consistency, and safety alignment.
- Optimize model inference costs using quantization, distillation, or speculative decoding.

1. Collaboration

- Work with cross-functional teams (product, data engineers, UX) to deploy AI solutions.
- Document technical processes and contribute to knowledge-sharing sessions.

Qualifications

- Education:



Bachelor’s/Master’s in Computer Science, Data Science, or related field.
- Technical Skills:
- Proficiency in Python and familiarity with AI/ML libraries (PyTorch, TensorFlow).
- Basic understanding of NLP (tokenization, attention mechanisms) and neural architectures (Transformers, GANs).
- Experience with cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML).
- Proficiency in prompt engineering tools: LangChain, DSPy, Guidance, or LMQL.
- Experience with AI deployment tools: FastAPI, Docker, or MLflow for model serving
- AI/GenAI Exposure and experience with at least two of the following:
- Hands-on projects with LLMs (fine-tuning, prompt engineering) or diffusion models.
- Familiarity with vector databases (Pinecone, Milvus) and orchestration tools.
- Fine-tuning/training LLMs (e.g., Llama 2, Mistral) using LoRA, QLoRA, or RLHF.
- Building RAG pipelines with vector DBs (Pinecone, Weaviate) and embedding models (BERT, OpenAI text-embedding).
- Developing applications with diffusion models (Reliable Diffusion, DALL-E) or autoregressive architectures (GPT variants).
- Contributions to NLP projects (sentiment analysis, NER, text summarization) using libraries like spaCy or NLTK.
- Soft Skills:
- Strong problem-solving abilities and curiosity about emerging AI trends.
- Ability to communicate technical concepts to non-technical stakeholders.

Preferred Qualifications Additions

- Certifications:
- Azure: Microsoft Certified: Azure AI Engineer Associate.
- GCP: Google Cloud Professional Machine Learning Engineer.

📌 Generative AI Engineer (Delhi)
🏢 XCEEDANCE
📍 Delhi