Data Science Analyst

Role Overview

We seek a motivated Data Science analyst to design, implement, and optimize cutting-edge generative AI solutions. Youll 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.

2. 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).

1. Evaluation & Optimization

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

2. 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

  : Bachelors/Masters 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.