Data Scientist - GenAI

Job Description

We are seeking a highly motivated Data Scientist specialized in building production-ready Generative AI applications. You will be responsible for developing and deploying advanced RAG systems, LLM-powered applications, and machine learning models that drive measurable business impact.

Key Responsibilities

GenAI Application Development

• Design and implement production RAG systems using LangChain, LlamaIndex, and vector databases (Pinecone, Qdrant, Weaviate)
• Develop LLM-powered applications with proper prompt engineering, safety guardrails, and output evaluation frameworks
• Build and optimize LLM chains and pipelines for complex multi-step reasoning and agent-like behaviors
• Implement fine-tuning workflows using LoRA, QLoRA, and parameter-efficient training techniques
• Create robust prompt templates and evaluation systems to ensure consistent, high-quality outputs at scale

Production & Operations (LLMOps)

• Deploy and monitor LLM applications using MLOps best practices and specialized LLMOps tooling
• Implement model versioning, A/B testing, and continuous evaluation pipelines for GenAI systems
• Manage computational resources and costs for large-scale model inference and training
• Establish safety protocols including bias detection, content filtering, and adversarial prompt detection

Collaboration & Impact

• Collaborate with cross-functional teams to understand business requirements and translate them into AI solutions
• Design and conduct experiments to measure GenAI application performance and business impact
• Stay current with rapid advancements in GenAI, LLMs, and emerging AI safety practices

Required Qualifications

Core Technical Skills

• Advanced Python proficiency with deep expertise in data manipulation (Pandas, NumPy) and ML frameworks (PyTorch, TensorFlow)
• Production experience with GenAI frameworks: LangChain, LlamaIndex, or similar RAG-focused platforms
• Hands-on experience with vector databases (Pinecone, Qdrant, Weaviate, Elasticsearch) and semantic search
• LLM fine-tuning experience with techniques like LoRA, QLoRA, and RLHF

GenAI & LLMOps Knowledge

• Deep understanding of LLM architectures including transformer models, attention mechanisms, and modern foundation models
• Experience with model evaluation and testing for LLM applications, including bias detection and safety assessment
• Knowledge of LLMOps practices including model monitoring, versioning, and deployment strategies
• Understanding of AI safety principles including prompt injection defense, content filtering, and ethical AI practices

Traditional ML & Data Science

• Solid foundation in statistical modeling and time series analysis
• Experience with optimization techniques and algorithms
• Strong problem-solving and analytical skills with proven ability to translate business problems into technical solutions

Collaboration & Communication

• Excellent communication skills with ability to explain complex GenAI concepts to non-technical stakeholders
• Experience working in cross-functional teams and agile development environments
• Track record of delivering production AI systems that create measurable business value

Preferred Qualifications

Advanced Education & Certification

• Advanced degree in Computer Science, Statistics, Mathematics, AI/ML, or related quantitative field
• Relevant certifications in cloud AI platforms (AWS Bedrock, Google Vertex AI, Azure Foundry)
• Specialized training in AI safety, responsible AI, or LLMOps frameworks

Advanced Technical Experience

• Experience with multi-modal AI applications combining text, image, and audio modalities
• Knowledge of distributed computing frameworks (Dask, Ray, Spark) for large-scale AI workloads
• Experience with agentic AI systems and complex reasoning frameworks
• Background in AI red teaming or adversarial testing of LLM systems
• Familiarity with edge deployment and model optimization techniques (quantization, pruning)

Industry & Domain Expertise

• Experience in high-stakes production environments (finance, healthcare, legal) with strict compliance requirements
• Knowledge of regulatory frameworks affecting AI deployment (GDPR, AI Act, algorithmic accountability)
• Track record of scaling GenAI solutions from prototype to enterprise production
• Experience with cost optimization strategies for large-scale LLM deployment

Technical Infrastructure

• Cloud platform expertise (AWS, GCP, Azure) with focus on AI/ML services and GPU optimization
• Containerization and orchestration experience (Docker, Kubernetes) for scalable AI deployments
• Experience with specialized AI hardware (A100s, H100s) and distributed training setups
• Knowledge of CI/CD pipelines specifically designed for ML and LLMOps workflows

What Sets This Role Apart

This position represents the cutting edge of applied AI, where you'll work with the latest GenAI technologies to solve real business problems. You'll be part of building the next generation of AI applications that combine the creativity of LLMs with the precision of traditional ML, all while maintaining the highest standards of safety, ethics, and reliability.

Continuous Learning Culture: Given the rapid pace of GenAI advancement, we foster a culture of continuous learning with dedicated time for research, experimentation, and staying current with the latest developments in the field.

If you're passionate about pushing the boundaries of what's possible with GenAI, have hands-on experience building production AI systems, and are excited to tackle complex challenges at the intersection of language models and business impact, we encourage you to apply.