Location: Hybrid / Remote
Experience: 8–10+ years
Company: Open Insights
Apply at: [email protected]
We are particularly interested in candidates who can bridge the gap between business strategy and modern AI capabilities, including GenAI and agent-based systems.
About Open Insights
Open Insights is a data and AI consulting firm focused on helping organizations unlock real business value through data-driven decision-making and scalable AI solutions. We work at the intersection of strategy, data, and AI, delivering practical and production-ready solutions.
We are looking for a Senior Data & AI Consultant with 8–10+ years of experience to lead and deliver data, analytics, and AI transformation initiatives, including modern AI and GenAI use cases.
This role requires a strong blend of consulting, strategic thinking, and hands-on understanding of modern data and AI ecosystems.
- Lead Data & AI strategy and transformation engagements
- Identify and prioritize high-value AI / GenAI use cases
- Design data platforms, AI architectures, and implementation roadmaps
- Translate business problems into data science and AI solutions
- Drive end-to-end delivery from use case definition to deployment
- Work closely with engineering teams on model development and MLOps
- Engage senior stakeholders and support decision-making with data insights
- Support pre-sales, proposals, and thought leadership
- Define KPIs, value tracking, and ROI measurement frameworks
- Ensure adherence to model governance, explainability, and scalability standards
- 8–10+ years of experience in data, analytics, AI, or consulting roles
- Strong experience in client-facing consulting and stakeholder engagement
- Proven track record in delivering data & AI transformation programs
Strong understanding of:
- Data strategy, data architecture, and data platforms
AI/ML lifecycle (problem framing model deployment- monitoring)
- Advanced analytics and statistical modeling
- Business translation of AI use cases
AI / Data Science & Technical Expertise
- Candidates should have hands-on or strong conceptual understanding of:
- Machine Learning & Data Science
- Supervised & unsupervised learning (classification, regression, clustering)
- Time series forecasting, anomaly detection
- Model evaluation, feature engineering, hyperparameter tuning
- Experience with libraries: Scikit-learn, XGBoost, LightGBM, TensorFlow, PyTorch
GenAI & LLM Ecosystem
- Experience with LLMs (GPT, Llama, Claude, etc.)
- Prompt engineering and evaluation techniques
- Retrieval-Augmented Generation (RAG) architectures
- Vector databases (FAISS, Pinecone, Weaviate)
- Frameworks: LangChain, LlamaIndex, Semantic Kernel
- Fine-tuning / instruction tuning concepts (LoRA, adapters)
MLOps & Model Lifecycle
- Model versioning, experiment tracking (MLflow, Weights & Biases)
- CI/CD for ML pipelines
- Monitoring model performance and drift
- Deployment patterns (APIs, batch, real-time)
Tools & Platforms (Preferred Exposure)
- Cloud Platforms: AWS, Azure, or GCP
- Data Stack: SQL, Python, Spark, Databricks
- Visualization: Power BI, Tableau
- AI Coding Tools: GitHub Copilot, Cursor, ChatGPT
- Workflow/Orchestration: Airflow, Prefect
- Version Control: GitHub / GitLab
- Experience in GenAI use cases (chatbots, copilots, document AI, agents)
- Exposure to data governance, model risk, and responsible AI practices
- Experience in public sector, mobility, or smart city domains (plus)
- Familiarity with Agile / Scrum delivery models
- Background in top-tier consulting firms is a plus
