Apply Now: https://in.indeed.com/job/lead-ai-engineer-bda6ee6a9a87ff43
About Us
- Neuralcraft builds intelligent systems that bring human understanding to AI-driven workflows. From healthcare, insurance to e-commerce and citizen services, we design and deploy AI agents that reason, act, and explain their decisions enabling organizations to automate complex operations while keeping humans in the loop.
- Our products power personalized recommendations, automated decision support, and intent-driven experiences that connect people, products, and services with precision and empathy. Teams use Neuralcraft to turn raw data and business rules into adaptive intelligence layers that understand context, reduce manual work, and improve outcomes.
- We’re a growing team of engineers, designers, and AI researchers passionate about human-centric automation. Our experience spans applied AI, healthcare intelligence, and systems design. Working at Neuralcraft means helping shape the future of explainable, actionable AI - from building real-time intelligence layers for payers to creating hyperpersonalized retail systems that truly understand intent.
What You’ll Do
- Design and build end-to-end AI systems that power real-world, production-grade use cases across industries like healthcare, insurance, and commerce.
- Prototype and experiment - rapidly turn new ideas into functional proof-of-concepts that push the boundaries of human-centric automation.
- Evaluate and fine-tune models, from classical ML pipelines to LLM-based reasoning systems, optimizing for performance, explainability, and reliability.
- Develop rigorous evaluation frameworks to ensure model behavior, alignment, and performance meet production standards.
- Deploy monitoring and observability tools for AI systems - tracking anomalies, staleness, and drift to maintain model health over time.
- Design agentic workflows and dynamic context engines that enable adaptive, intent-aware automation for business processes.
- Lead and mentor a small team of engineers through the full lifecycle - from architecture and experimentation to deployment, monitoring, and iteration