• Lead technical architecture and decision‑making for complex GenAI engagements • Deliver production‑ready AI applications—including RAG, conversational AI, and agentic systems—under tight timelines • Implement advanced GenAI patterns using vector databases, orchestration frameworks, and managed AI services with strong observability, security, and optimization • Integrate LLM APIs and AI services into existing workflows across cloud and on‑prem environments; design responsible AI guardrails and evaluation frameworks • Conduct hands‑on training, documentation, and mentorship to ensure sustainable knowledge transfer • Maintain and expand reusable AI solution templates and technical standards • Validate program team readiness for independent AI operations • Experiment with emerging AI tools and share findings through demos, documentation, and discussions

• As a Senior Generative AI Engineer, you will lead the design and deployment of advanced AI solutions in cloud environments like AWS, Azure, and GCP. • Responsibilities include building robust data pipelines, optimizing performance, and deploying large language models (LLMs) for various business needs. • Emphasizing AI ethics, you'll ensure responsible practices are integrated throughout development. • Additionally, you'll collaborate with stakeholders to translate business needs into technical requirements and communicate complex AI concepts clearly.

• Design and implement end-to-end AI solutions for document understanding and automated report generation. • Build and deploy LLM-based systems, including RAG pipelines, to retrieve and combine context from multiple data sources. • Work with unstructured and semi-structured data such as PDFs, documents, images, and historical records, transforming it into usable inputs for AI systems. • Rapidly prototype, test, and iterate on AI solutions to deliver measurable improvements. • Integrate AI outputs into existing systems via APIs, collaborating with backend teams working in C# and SQL Server environments. • Ensure solutions are scalable, reliable, and aligned with real-world business workflows. • Address performance, accuracy, and usability challenges in AI-generated outputs. • Operate within data privacy and security constraints, including handling sensitive information and exploring local model deployment where required. • Collaborate with internal teams to understand requirements and refine use cases. • Communicate technical concepts, trade-offs, and solution approaches clearly to non-technical stakeholders. • Contribute to shaping the organisation’s approach to AI adoption and best practices.

• Drive revenue growth in the U.S. West Coast region • Partner with enterprise customers, research institutions, and cloud organizations • Collaborate with internal engineering teams and solutions architects