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Job requirements
- Brillio is the partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Backed by Bain Capital private equity, and growing at nearly 60% YoY since its inception, Brillio is one of the fastest growing digital technology service providers. We help clients harness the transformative potential of the four superpowers of technology – cloud computing, internet of things (IoT), artificial intelligence (AI), and mobility. Born digital in 2014, we apply Customer Experience Solutions, Data Analytics and AI, Digital Infrastructure and Security, and Platform and Product Engineering expertise to help clients quickly innovate for growth, create digital products, build service platforms, and drive smarter, data-driven performance.
- With delivery locations across the United States, Romania, Canada, Mexico, and India, our growing global workforce of over 6,000 Brillians blends the latest technology and design thinking with digital fluency to solve complex business problems and drive competitive differentiation for our clients. Brillio was awarded ‘Great Place To Work’ in 2021 and 2022. Learn more www.Brillio.com
We are seeking a highly accomplished Principal Data Scientist with extensive experience in Agentic AI, Large Language Models (LLMs), and enterprise-grade AI/ML systems. The ideal candidate will lead the design, experimentation, and deployment of autonomous, multi-agent AI systems leveraging LLMs, knowledge graphs, and reasoning frameworks. This is a strategic and hands-on role at the intersection of data science, AI research, and engineering leadership.
Key Responsibilities
- Lead the architecture, experimentation, and fine-tuning of LLMs (e.g., GPT, Claude, Mistral, LLaMA, Falcon) for business-specific applications.
- Drive Agentic AI POCs and production implementations using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or Semantic Kernel.
- Design and implement multi-agent systems with human-in-the-loop decision-making, contextual reasoning, and memory management.
- Collaborate with cross-functional AI engineering, data platform, and product teams to operationalize LLM-based solutions.
- Develop fine-tuning and RAG pipelines using open-source and proprietary foundation models.
- Lead research and evaluation of emerging AI/LLM technologies for internal innovation and client solutions.
- Mentor data scientists and ML engineers on prompt engineering, model alignment (RLHF/RLAIF), and scalable AI system design.
- Partner with business stakeholders to translate complex business problems into data-driven, AI-enabled strategies.
- Publish internal whitepapers and drive AI Center of Excellence (CoE) initiatives within the organization.
Required Skills & Experience
- 14–22 years of overall experience with at least 4+ years in advanced AI/LLM/GenAI research or engineering.
- Deep expertise in Agentic AI and building multi-agent workflows using LangGraph, LangChain, AutoGen, CrewAI, or Haystack.
- Strong hands-on programming skills in Python, with working proficiency in R or Scala.
- Proven experience with LLM fine-tuning, adapter methods (LoRA, QLoRA, PEFT), RAG pipelines, and vector databases (e.g., Pinecone, FAISS, Weaviate, Milvus).
- Strong understanding of transformer architectures, tokenization, prompt optimization, and model evaluation metrics.
- Experience integrating LLMs with enterprise data systems, APIs, and orchestration pipelines (Databricks, AWS, Azure ML, Vertex AI).
- Demonstrated success leading POCs and production-grade implementations in Agentic AI use cases (knowledge assistants, automation, data analysis, etc.).
- Familiarity with data engineering, MLOps/LLMOps, and cloud-native AI deployment.
- Excellent analytical, communication, and leadership skills with a track record of mentoring teams and driving innovation.
Preferred Qualifications
- Advanced degree (Ph.D./M.Tech/M.S.) in Computer Science, AI, Machine Learning, Data Science, or a related field.
- Publications, patents, or conference presentations in AI, NLP, or LLM domains.
- Experience in Generative AI governance, ethics, or AI system reliability.
- Hands-on experience with open-source LLM frameworks and custom dataset curation for fine-tuning.
