Data Science Specialist

Job Description

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 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. Tooling & Integration Build applications using tools like LangChain, LlamaIndex, or Hugging Face Transformers. Integrate GenAI APIs (OpenAI, Anthropic, Mistral) into enterprise workflows. 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). Evaluation & Optimization Develop metrics for hallucination reduction, output consistency, and safety alignment. Optimize model inference costs using quantization, distillation, or speculative decoding. 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 (Stable 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.