AI/ML Solutions Architect

We are looking for an experienced Senior Solution Architect with deep expertise in AI, Machine Learning, Generative AI, and Agentic AI to join our team. This is a senior technical leadership role focused on designing, developing, and scaling AI/ML solutions for complex enterprise environments across various industries., * Client Engagement & Discovery: Work directly with clients to understand business needs and identify opportunities for AI/ML/GenAI/Agentic AI adoption.

• Solution Design & Architecture: Lead the design of scalable, secure, and production-grade AI/ML solutions.
• Proposal Management: Lead the technical response to RFPs/RFIs and support pre-sales efforts.
• Project Leadership: Define project scope, resource planning, risk management, and delivery timelines.
• Technical Mentorship: Provide guidance and mentorship to engineering and data science teams.
• Hands-on Involvement: Contribute to architecture reviews, prototyping, and critical solution components.
• Cross-Team Collaboration: Partner with sales, product, engineering, and external technology partners to expand solution capabilities.
• Thought Leadership: Represent the organization in workshops, conferences, and technical forums.

• 5+ years of experience designing and deploying AI/ML solutions at scale.
• Strong hands-on experience with modern ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and GenAI tools.
• Familiarity with LLMs, Retrieval-Augmented Generation (RAG), vector databases, multi-agent systems, evaluations.
• Proficient in Python.
• Experience with LLM app development frameworks such as LangChain, LangGraph, PydanticAI
• Proven ability to translate complex business requirements into scalable technical solutions.
• Excellent communication skills, including experience engaging with executive-level stakeholders.
• Background in consulting or client-facing technical roles., * Experience in one or more industries: Pharma, Technology, Media, Retail, BFSI, or Travel & Hospitality.
• Knowledge of cloud platforms (AWS, Azure, GCP), including MLOps practices.
• Knowledge of AI governance, ethics, and regulatory compliance (e.g., GDPR, CCPA).
• Experience with data engineering, large-scale data infrastructure, and data governance.
• Understanding of AI project economics and business case development.