Role Summary
Seeking an experienced Data Scientist to design, build, and operationalize secure Generative AI and ML solutions for the enterprise intranet and mobile applications. The role focuses on knowledge retrieval, document intelligence, intelligent Q&A, and automation of internal workflows, with strong emphasis on privacy, security, and MLOps best practices.Key ResponsibilitiesRequirements & Architecture
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Audit existing intranet, data sources, and content repositories to assess AI readiness and integration points.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Design end‑to‑end AI/GenAI architecture including local or hosted LLM integration, vector databases, retrieval pipelines, and secure API layers for intranet and mobile clients.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Define requirements for knowledge retrieval, summarization, intelligent Q&A, and task automation in collaboration with business and IT stakeholders.
Development & Integration
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Integrate enterprise‑grade LLMs (e.g., Azure OpenAI, Hugging Face models, Vertex AI) using frameworks such as LangChain and LangGraph.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Build secure connectors to internal systems (SharePoint, document servers, ticketing tools, intranet CMS) and implement RAG pipelines over internal data.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Develop and expose ML/GenAI services via FastAPI, including RESTful endpoints consumed by intranet portals and mobile apps.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Create user-facing experiences such as chatbots, virtual assistants, and search interfaces embedded within intranet and mobile channels.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Implement fine‑tuning, prompt engineering, and evaluation workflows for LLM‑based use cases like document summarization, classification, and ticket triage.
Governance, Testing & Validation
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Define and execute evaluation strategies for AI outputs, including accuracy, relevance, bias, and safety metrics for internal users.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Implement access control, data governance, and policy‑driven guardrails across the AI lifecycle, ensuring privacy, compliance, and secure data handling.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Optimize model and service performance (latency, throughput, cost) for on‑premise or intranet‑restricted environments.
Deployment, MLOps & Support
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Establish and maintain MLOps pipelines (CI/CD for ML), including model training, versioning, deployment, monitoring, and rollback strategies.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Deploy AI services within Azure or GCP environments, including on‑prem/hybrid setups that serve intranet and mobile applications securely.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Monitor production systems (drift, performance, reliability) and provide ongoing support, incident resolution, and continuous improvement of AI solutions.
Required Qualifications & Experience
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> 8+ years of experience in AI/ML engineering or data science, including at least 2 years in a senior or lead role delivering production systems.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Strong proficiency in Python and ML/deep learning frameworks such as PyTorch, TensorFlow, and Scikit‑learn.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Proven experience building and serving ML/GenAI APIs using FastAPI in production environments.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Hands‑on experience with Generative AI and LLM ecosystems: Hugging Face Transformers, LangChain and/or LangGraph, Azure OpenAI, Vertex AI, or similar platforms.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Solid background in MLOps practices: CI/CD for ML, ML pipelines, model registries (e.g., MLflow), monitoring, and observability.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Cloud expertise in Azure or GCP, including AI/ML and data services relevant to secure enterprise deployments.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Strong experience with vector databases and semantic search (e.g., Pinecone, ChromaDB, FAISS, Azure AI Search, OpenSearch) for knowledge retrieval use cases.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Demonstrated knowledge of prompt engineering, RAG architectures, fine‑tuning, and structured evaluation of LLM applications.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Prior experience building enterprise GenAI assistants, chatbots, or knowledge retrieval/search systems integrated with internal data and identity/access systems.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Excellent communication, leadership, and stakeholder management skills, including mentoring junior team members and collaborating with cross‑functional teams.
- p]:pt-0 [&>p]:mb-2 [&>p]:my-0"> Preferable: Contributions to open‑source projects in AI/ML/GenAI (e.g., libraries, examples, or tools) demonstrable via GitHub or similar platforms.
Skills: gcp,machine learning,pytorch,fastapi,python,scikit-learn,azure,azureopenai,mlops,ai/ml,ml,ai,tensorflow,llm,genai
