Senior Data Science Lead - R01551331

Brillio; Chennai District; On-site; INR 2,500,000 - 3,500,000; Full time

Job summary

A leading data science firm in Chennai is seeking a Senior Data Science Lead to spearhead the development of semi-autonomous AI solutions. The candidate will architect multi-agent systems, optimize workflows, and lead research initiatives in cutting-edge AI technologies. A strong knowledge in LangGraph, reinforcement learning, and decision-making capabilities is essential. This role focuses on translating AI innovations into impactful business solutions.

Qualifications

• Experience with multi-agent AI systems and LLM orchestration.
• Deep expertise in reinforcement learning and statistical analysis.
• Hands-on experience in developing AI solutions and workflows.

Responsibilities

• Design and develop multi-agent AI systems using LangGraph.
• Optimize agent orchestration workflows ensuring high performance.
• Lead AI research and drive innovation in Agentic AI projects.

Skills

Hypothesis Testing

Python/PySpark

Machine Learning Frameworks

Statistical analysis

Reinforcement Learning

Tools

LangGraph

KubeFlow

Pinecone

Job description

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

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.