AI Model Development Engineer

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

o Assist in developing, training, and fine-tuning generative models (text, image, code) using frameworks like PyTorch, TensorFlow, or JAX.

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

2. Tooling & Integration

o Build applications using tools like LangChain, LlamaIndex, or Hugging Face Transformers.

o Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enter-prise workflows.

3. Prompt Engineering

o Design and test advanced prompting strategies (e.G., few-shot learning, chain-of-thought, ReAct frameworks) for domain-specific tasks (legal, healthcare, finance).

o Create reusable prompt templates for common workflows (customer support, code generation, content moderation).

4. Evaluation & Optimization

o Develop metrics for hallucination reduction, output con-sistency, and safety alignment.

o Optimize model inference costs using quantization, distilla-tion, or speculative decoding.

5. Collaboration

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

o 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 :

o Proficiency in Python and familiarity with AI / ML libraries (PyTorch, TensorFlow).

o Basic understanding of NLP (tokenization, attention mecha-nisms) and neural architectures (Transformers, GANs).

o Experience with cloud platforms (AWS SageMaker, GCP Ver-tex AI, Azure ML).

o Proficiency in prompt engineering tools : LangChain, DSPy, Guidance, or LMQL.

o Experience with AI deployment tools : FastAPI, Docker, or MLflow for model serving

o Hands-on projects with LLMs (fine-tuning, prompt engineer-ing) or diffusion models.

o Familiarity with vector databases (Pinecone, Milvus) and or-chestration tools.

o Fine-tuning / training LLMs (e.G., Llama 2, Mistral) using LoRA, QLoRA, or RLHF.

o Building RAG pipelines with vector DBs (Pinecone, Weaviate) and embedding models (BERT, OpenAI text-embedding).

o Developing applications with diffusion models (Stable Diffusion, DALL-E) or autoregressive architectures (GPT variants).

o Contributions to NLP projects (sentiment analysis, NER, text summa-rization) using libraries like spaCy or NLTK.

o Strong problem-solving abilities and curiosity about emerging AI trends.

o Ability to communicate technical concepts to non-technical stakeholders.

Preferred Qualifications Additions

o Azure : Microsoft Certified : Azure AI Engineer Associate.

o GCP : Google Cloud Professional Machine Learning Engineer