Activate JobCopilot
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. ________________________________________ The 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.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Your email job alert is now active!
