Data Science Lead - R01561319

Brillio; Bengaluru; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

A leading tech company in Bengaluru is seeking a Data Science Lead to drive AI solution architecture and integration. The role requires expertise in hypothesis testing, proficiency in Python/PySpark, and experience with AI frameworks like TensorFlow. Key responsibilities include leading the design of autonomous AI agents, developing integration patterns, and collaborating with data scientists on model optimization. Ideal candidates will have strong statistical analysis skills and a passion for innovative applications of AI technology.

Qualifications

• Experience in designing AI-enabled applications and integration of AI agents.
• Strong knowledge of statistical analysis techniques and machine learning.
• Familiarity with orchestration layers for AI frameworks.

Responsibilities

• Lead architectural design for autonomous AI agents.
• Develop integration patterns for connected applications.
• Design chatbot interfaces and real-time transcription services.
• Partner with Data Scientists to optimize models.

Skills

Hypothesis Testing

Python/PySpark

Statistical analysis

Machine Learning Frameworks

Forecasting

Tools

TensorFlow

KubeFlow

BentoML

Job description

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

Specialization

• Data Science Advanced: Data Scientist

Job Requirements

Job Description: Lead Solution Architect (Agentic AI & App Integration) Role Overview As the Lead Solution Architect, you will be the visionary and technical engine behind our AI-Enabled application. Transform a reactive monitoring environment into a proactive, agentic ecosystem. You will design the foundational integration blocks that allow Generative AI agents to interact with third‑party applications, transcribe real‑time communications, and provide intelligent, multi‑step reasoning to support mission‑critical decisions.

Foundational Blocks

1. Orchestration Layer: Building the multi‑agent framework (e.g., AutoGen, LangChain, or Semantic Kernel) that allows AI agents to collaborate, hand off tasks, and resolve complex incidents autonomously.
2. Universal Integration Fabric: Designing a standardized API and Webhook gateway that connects apps for real‑time data ingestion and action execution.
3. The “Live Intelligence” Pipeline: Implementing Speech‑to‑Text (STT) and Natural Language Understanding (NLU) systems to ingest radio, video, and voice comms directly into the AI’s reasoning engine.
4. Governance & ALA (Agentic Level Agreements): Establishing the guardrails, audit logs, and “human‑in‑the‑loop” protocols to ensure AI actions are safe, compliant, and transparent.

Key Responsibilities

• Drive Agentic AI Strategy: Lead the architectural design of autonomous agents capable of L1/L2 incident triage, automated investigation, and proactive threat hunting.
• Cross‑App Integration: Develop reusable integration patterns (Event‑Driven, WebSockets, REST) to ensure the app is the “single pane of glass” for all connected applications.
• Conversational AI & STT: Design high‑fidelity chatbot interfaces and real‑time transcription services that allow operators to “talk to the data” and receive voice‑activated summaries of active incidents.
• Data Science Leadership: Partner with Data Scientists to fine‑tune LLMs, optimize Retrieval‑Augmented Generation (RAG) pipelines, and ensure model outputs are grounded in enterprise‑specific data.
• Scalability & Resilience: Ensure the architecture supports high‑concurrency, low‑latency operations.