The Role
Lead research, design, and deployment of multi-agent, agentic AI systems using LangGraph and LLM orchestration. Build memory-augmented agents, implement RAG with vector DBs, apply RLHF/RLAIF, prototype PoCs, scale production architectures, and mentor AI engineers.
Summary Generated by Built In
Senior Data Science Lead
Primary Skills
- Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio
Job requirements
- JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. ________________________________________ Key Responsibilities 1. Architecting & Scaling Agentic AI Solutions • Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. • Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. • Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization • Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. • Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. • Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research • Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. • Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. • Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact • Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. • Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building • Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. • Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents. ________________________________________
Skills Required
- Hands-on experience building multi-agent/agentic AI systems with LangGraph
- Deep expertise in LLM orchestration and retrieval-augmented generation (RAG)
- Experience with reinforcement learning fine-tuning (RLHF, RLAIF)
- Knowledge graphs and probabilistic graph models implementation experience
- Experience with vector databases: Pinecone, Weaviate, FAISS
- Strong Python and PySpark skills
- Proficiency with ML frameworks: TensorFlow, PyTorch, Scikit-Learn, Keras, CNTK, MXNet
- Familiarity with orchestration/deployment tools: Kubeflow, BentoML
- Experience with data quality and monitoring tools: Great Expectations, Evidently AI
- Statistical analysis and forecasting experience (ARIMA, ARIMAX, Exponential Smoothing)
- Experience with R and R Studio, and/or SAS/SPSS for statistical modeling
- Proven leadership and mentorship of AI Engineers/Data Scientists
Brillio Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Brillio and has not been reviewed or approved by Brillio.
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Healthcare Strength — Healthcare is considered comprehensive, including medical coverage for employees and dependents alongside life, disability, and accidental death protections. Feedback suggests these protections are a core strength of the package.
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Leave & Time Off Breadth — Time-off options include paid leave and parental leave, with flexible or ‘flexible PTO’ approaches cited in some contexts. Feedback suggests this breadth helps support work-life balance when team norms permit usage.
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Wellbeing & Lifestyle Benefits — Wellbeing offerings span counseling, financial-management sessions, fitness programs, and travel insurance, plus region-specific extras like discounted IT hardware and work-from-home essentials. Feedback suggests these add-ons enhance perceived value beyond core insurance.
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The Company
What We Do
Brillio is the leader in global digital business transformation, applying technology with a human touch. We help businesses define internal and external transformation objectives, and translate those objectives into actionable market strategies using proprietary technologies. With 2600+ experts and 13 offices worldwide, Brillio is the ideal partner for enterprises that want to quickly increase their core business productivity, and achieve a competitive edge, with the latest digital solutions.
