Design & Deliver AI Solutions: Build statistical, ML, and generative/agentic AI solutions spanning RAG pipelines, chat/assistants, classification, forecasting, and recommendation systems using a fit‑for‑purpose toolkit from traditional predictive modeling to agentic workflows.
Knowledge Base Engineering for Strategic Domains: Engineer and maintain domain‑specific knowledge bases (regulatory intelligence, competitive insights, customer sentiment) to power generative applications across underwriting, pricing, and service.
Prompt & Agent Design: Author robust system prompts, few‑shot patterns, and structured outputs (e.g., JSON schemas). Define safe tool‑use policies and function/structured calling for reliable agent behavior.
Synthetic Data Generation & Augmentation: Develop and validate synthetic data pipelines to alleviate sparsity and accelerate convergence, especially for low‑frequency perils and emerging segments, while preserving privacy and distributional fidelity.
Customer Experience Optimization: Apply GenAI to elevate self‑service, virtual assistants, and inspection automation, driving personalization, speed, and operational efficiency.
Architectural Collaboration & MLOps Integration: Partner with enterprise architects and platform teams to ensure scalable, secure deployments via unified systems. Standardize experiment tracking, registries, evaluation gates, and CI/CD patterns across clouds and services.
Innovation & Continuous Learning: Identify and pilot emerging methods (OCR, rerankers, PEFT/LoRA, distillation). Build reusable accelerators (chunking templates, prompt registries, evaluation harnesses). Stay current on AI/ML, LLMOps, NLP, RAG, and responsible AI.
Required Skills & Experience :
Experience Range - 6 to 9 Years
Experience in statistical modeling and machine learning using Python, including extensive use of pandas, NumPy, scikit-learn, and strong SQL for data exploration, feature development, and knowledge preparation; familiarity with PyTorch and/or TensorFlow preferred.
Experience across the end-to-end modeling lifecycle, including problem framing and requirements gathering, experiment design, offline evaluation, and ongoing production validation and monitoring.
Solid understanding and practical application of core machine learning methods, with 3+ years of experience applying deep learning architectures in real-world use cases.
Experience designing and operationalizing model evaluation and monitoring approaches, including test set creation (gold and/or synthetic), metric definition and tracking (e.g., classification, forecasting, ranking/IR, and business KPIs), and supporting A/B testing, drift detectio
Additional Information
IND Staff Engineer - GCC094
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Thehartford · Gcc-puppalaguda Village, India