Lead the end-to-end architecture for AI-enabled platforms supporting various use cases (SDLC/PDLC, QE, Conversational AI, copilots)
Define scalable, reusable architectural patterns for conversational assistants, generative insights, predictive analytics
Lead the design and implementation of AI orchestration frameworks to enable scalable, multi-step reasoning and agent-based workflows
Architect solutions using frameworks such as LangGragh (or equivalent) and cloud-native capabilities (e.g., managed AI/agent services)
Design workflows incorporating: Retrieval-augmented generation (RAG) across structured and unstructured data
Define and own model selection, evaluation, and benchmarking frameworks, balancing performance, cost, latency, explainability, and risk
Establish and mature LLMOps / MLOps practices, including versioning, monitoring, evaluation, logging, rollback strategies, and cost controls
Ensure platforms meet enterprise standards for availability, scalability, performance, resilience, and reliability
Translate business challenges into high-impact AI use cases
Guide experimentation and proof-of-concepts while ensuring a clear path to production
Stay current on emerging AI technologies and recommend pragmatic adoption strategies

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field
17-20 years of experience designing and delivering enterprise software or data platforms, with 2+ years focused on AI / ML solutions
Hands-on experience with large language models and modern AI frameworks (prompt engineering, embeddings, RAG, agents)
Strong background in cloud platforms (Azure or AWS) and microservices-based architectures
Experience operationalizing AI systems with MLOps / LLMOps tooling and practices
Solid understanding of data engineering, APIs, and distributed systems
Proven ability to design systems with security, privacy, and regulatory considerations
Familiarity with AI governance frameworks
Experience with vector databases, semantic search, or enterprise data catalogs
Exposure to process mining, control testing automation, or continuous auditing
Cloud or AI/ML certifications

Lead AI Solutions Engineer – Assistant Vice President

Key skills