Principal AI/ML Solution Architect

Client iQuest Solutions

Engagement Enterprise Data & AI

Location Hyderabad, India (Comfortable with monthly travel)

Work Type / Hours Contract | US Central Time (CST/CDT)

The Role As Principal AI/ML Solution Architect, you will be the senior technical leader responsible for architecting and guiding enterprise Data Science, Machine Learning, Generative AI, and agentic AI solutions. You will work across the complete solution lifecycle from understanding business problems and assessing data to defining the ML/AI approach, architecture, implementation strategy, production deployment, and ongoing operations.

This role requires a strong hands-on background in Data Science and ML combined with architecture and technical leadership experience. The ideal candidate has personally built and delivered production ML systems before moving into broader AI/ML architecture responsibilities, and has recent practical experience implementing LLM/GenAI and agentic AI solutions.

The role is client-facing and requires the ability to work with engineering teams, client architects, business stakeholders, and executives to convert complex or ambiguous requirements into scalable production solutions.

Key Responsibilities

• Own end-to-end architecture for enterprise Data Science, ML, Generative AI, and agentic AI solutions across business and manufacturing use cases.
• Translate business problems into practical AI solutions covering use-case definition, data assessment, modeling approach, architecture, implementation, deployment, monitoring, and continuous improvement.
• Architect production ML systems including predictive maintenance, anomaly detection, forecasting, computer-vision quality inspection, and process optimization.
• Guide ML/Data Science approaches including feature engineering, model selection, validation, experimentation, classical ML, time-series methods, and deep learning where appropriate.
• Design enterprise GenAI solutions using RAG, embeddings/vector search, retrieval strategies, prompt/context engineering, LLM evaluation, guardrails, observability, and LLMOps.
• Architect agentic AI solutions using tool/function calling, orchestration, state and memory, multi-step workflows, human-in-the-loop patterns, and API/MCP integrations.
• Define production ML architecture including training and inference pipelines, model serving, monitoring, drift detection, retraining, model lifecycle management, and CI/CD.
• Provide hands-on technical direction to Data Scientists, ML Engineers, GenAI/Agentic AI Engineers, Data Engineers, and Cloud Engineers; review solution designs, modeling approaches, and critical implementations.
• Design cloud-native AWS architectures for AI/ML workloads using SageMaker, Bedrock, S3, Glue, EMR, Redshift, Kinesis/MSK, Lambda, ECS/EKS, IAM, and related services as appropriate.
• Establish architecture standards, design reviews, technology decisions, evaluation practices, and production-quality expectations for AI/ML delivery.
• Ensure solutions meet enterprise requirements for scalability, reliability, security, cost efficiency, governance, observability, and maintainability.
• Serve as a senior technical advisor to client architects, IT/OT teams, business leaders, and executive stakeholders; clearly communicate architecture decisions, trade-offs, risks, and progress.

Required Qualifications

• 12+ years of overall technology experience with significant experience in Data Science, ML Engineering, AI Engineering, or related disciplines.
• 7+ years of direct experience designing, building, and delivering ML/Data Science solutions, including production-grade systems not only proofs of concept.
• 3+ years in AI/ML architecture, solution architecture, principal/lead engineering, or an equivalent technical leadership role.
• At least 1+ year of direct project experience implementing production LLM/Generative AI and agentic AI solutions.
• Strong ML/Data Science fundamentals including supervised/unsupervised learning, feature engineering, model selection and validation, experimentation, forecasting/time-series, and deep learning where relevant.
• Strong GenAI/LLM experience including RAG, embeddings, vector search/databases, retrieval design, prompt/context engineering, LLM evaluation, guardrails, and production LLM application patterns.
• Practical agentic AI experience including orchestration frameworks, tool/function calling, state/memory, …