Data Scientist

Role Overview

We are seeking a Data Scientist with 3–6 years of experience to design, build, and deploy GenAI and LLM-powered solutions. The role combines applied ML, prompt engineering, and MLOps to deliver scalable, production-grade AI systems.

Responsibilities

• Develop and deploy LLM-based applications (RAG, chatbots, copilots, summarization, search)
• Fine-tune, evaluate, and optimize LLMs and transformer models
• Design prompt engineering strategies and guardrails for safe, reliable outputs
• Build retrieval pipelines using vector databases (FAISS, Pinecone, etc.)
• Implement end-to-end ML pipelines (data model deployment monitoring)
• Collaborate with engineering teams to integrate models via APIs and microservices
• Monitor model performance, drift, and costs; implement feedback loops
• Ensure responsible AI practices (bias mitigation, explainability, governance)

Requirements

Required Skills & Qualifications

• 3–6 years of experience in Data Science / ML Engineering / Applied AI
• Strong programming in Python
• Hands-on experience with LLMs (OpenAI, Azure OpenAI, etc.)
• Experience with transformers, embeddings, and NLP pipelines
• Familiarity with RAG architectures and vector search
• Solid foundation in ML algorithms, statistics, and evaluation metrics
• Strong SQL and data handling skills

MLOps & Engineering Skills

• Experience with model deployment (FastAPI, Flask, Docker, Kubernetes)
• Exposure to CI/CD pipelines for ML (GitHub Actions, Azure ML, SageMaker, etc.)
• Experience in model monitoring, logging, and versioning (MLflow, Weights & Biases)
• Knowledge of data pipelines (Airflow, Spark)
• Familiarity with cloud platforms (AWS, Azure, or GCP)

Preferred Qualifications

• Experience fine-tuning LLMs (LoRA, PEFT techniques)
• Exposure to multi-modal models (text + image/audio)
• Knowledge of AI safety, hallucination mitigation, and evaluation frameworks
• Domain experience in enterprise use cases (support, search, analytics, automation)

Soft Skills

• Strong problem-solving and experimental mindset
• Ability to translate ambiguous business problems into AI solutions
• Excellent communication with technical & non-technical stakeholders

Education

• Bachelor's or Master's in Computer Science, AI, Data Science, Statistics, or a related field