Principal Data ScientistPrimary Skills-Principal Data Scientist + Agentic AI
-Lead Data Scientist + LangChain OR LangGraph
-LLM + fine-tuning + "production
-multi-agent OR autonomous agents + LLM"
-RAG pipeline
-GenAI + leadership + "MLOps"
Specialization1. Agentic AI (multi-agent workflows, autonomous agents, tool-using agents)
2. LLMs (fine-tuning, prompt engineering, RAG pipelines, production deployment)
3. LangChain / LangGraph / LlamaIndex / CrewAI / AutoGen
4. MLOps / LLMOps (model monitoring, CI/CD, versioning)
Job requirements- The Principal Data Scientist will play a critical role in translating complex business problems into scalable, AI-driven solutions. This role demands strong leadership, deep expertise in Agentic AI, Large Language Models (LLMs), and advanced analytics, along with the ability to influence senior stakeholders and drive measurable business outcomes.
Core Responsibilities
- Problem Formulation: Translate business objectives into well-defined data science, ML, and Agentic AI problems; validate OKRs using robust statistical and experimental measures.
- Agentic AI & LLM Solutions: Design, build, deploy, and optimize Agentic AI systems (multi-agent workflows, task orchestration, autonomous decision-making) using LLMs for real-world enterprise use cases.
- LLM Development & Deployment: Fine-tune, prompt-engineer, evaluate, and productionize LLMs (open-source or proprietary) for use cases such as copilots, RAG pipelines, conversational AI, and intelligent automation.
- Data Wrangling & Feature Engineering: Handle structured and unstructured data at scale, including text, documents, and conversational data for LLM-powered solutions.
- Insight Generation & Data Storytelling: Convert complex analytical outputs and AI model results into clear, compelling narratives for business and executive audiences.
- Technical Decision-Making: Make informed trade-offs on model complexity, iteration depth, experimentation cycles, and time-to-value.
- Design Thinking & Innovation: Apply design thinking principles to build user-centric AI products and data solutions.
- Mentorship & Leadership: Coach senior data scientists, review architectures, and establish best practices across data science, ML, and GenAI initiatives.
Key Qualifications - Technical Expertise
- Principal Data Scientist with strong hands-on experience in LLMs and Agentic AI.
- Proven experience in deploying, fine-tuning, and operationalizing LLMs in at least one real-world, production-grade project.
- Hands-on expertise in Agentic AI frameworks and patterns, such as multi-agent systems, autonomous workflows, tool-using agents, and human-in-the-loop architectures.
- 14-18 years of experience in Data Science, Machine Learning, Advanced Analytics, or AI, with at least 5 years in a technical leadership role.
- Proficiency in Python (mandatory); exposure to R or Scala is a plus.
- Strong foundation in machine learning, deep learning, NLP, and statistical modeling.
- Experience with LLM ecosystems and frameworks (e.g., LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Hugging Face).
- Expertise in cloud platforms (AWS, Azure, or GCP), big data technologies, and MLOps / LLMOps (model monitoring, versioning, CI/CD, cost optimization).
- Hands-on experience with TensorFlow, PyTorch, Scikit-learn, and related ML frameworks.
- Strong understanding of Responsible AI, model governance, explainability, and risk management, especially for GenAI solutions.
Leadership & Communication
- Proven ability to lead, inspire, and scale high-performing data science and AI teams.
- Exceptional communication and storytelling skills, with the ability to influence senior leadership and business stakeholders.
- Demonstrated experience mentoring, coaching, and upskilling data scientists and ML engineers.
- Strong business acumen with the ability to quickly understand new domains and apply AI effectively.
Education
- Master's or Ph.D. in Data Science, Computer Science, Statistics, Mathematics, or a related field.
Critical Leadership Qualities
- Customer Advocacy: Drives customer success by anticipating both stated and unstated business needs using AI-led, market-leading solutions.
- Owner's Mindset: Demonstrates deep ownership of outcomes and makes decisions aligned with Brillio's long-term success.
- Innovation Ethos: Embraces ambiguity, experiments relentlessly, and challenges the status quo-especially in GenAI and Agentic AI adoption.
- Execution with Pace: Delivers high-quality, production-ready AI solutions in fast-changing environments.
- Big Picture Thinking: Aligns AI, data, and business strategy to create shared success and ambitious growth targets.
- Winning Through Teams: Builds a collaborative culture that empowers teams to excel and innovate together.
