• Own end-to-end delivery of features and projects: design, implementation, deployment, and the client conversation around them • Pair with senior engineers on complex builds, owning meaningful slices independently • Build production AI workflows: RAG components, integrations across cloud platforms, agentic automations • Review pull requests from associate engineers • Write design notes that the team uses; contribute to architectural conversations

• Define and execute a company-wide AI strategy that ensures alignment between business objectives and technological innovation. • Spearhead transformational projects and deliver quantifiable value through AI across the organization. • Identify and prioritize high-impact AI use cases to reduce manual work and improve quality. • Ensure that AI initiatives translate into tangible outcomes such as productivity gains and cost efficiencies. • Create reusable AI tools to enable scalable adoption. • Lead an AI governance forum to prioritize use cases and manage resources. • Operationalize AI and integrate it into workflows. • Collaborate with HR and leadership for fostering AI literacy across the organization. • Measure metrics such as adoption and productivity uplift. • Ensure secure and responsible AI adoption by working closely with legal and data protection teams.

• Design, build, and operate RAG solutions across semantic search to retrieval over structured data and knowledge graphs • Build agentic systems that perform multi-step tasks against enterprise data • Drive automation of internal processes and ship AI features into customer-facing products • Own retrieval quality end to end: chunking strategies, hybrid approaches, and evaluation • Evaluate and recommend tooling for AI stack and drive decisions together with architect • Author production services in Go (gRPC) and integrate them with AI router and existing platform

• Lead technical discovery sessions with enterprise customers to understand business objectives, deployment requirements, and success criteria. • Scope and execute proof-of-concepts, pilot programs, and production deployment initiatives. • Conduct load testing and evaluations to validate model architectures and deployment configurations. • Design and implement end-to-end AI solutions within complex enterprise environments. • Build production-grade AI and machine learning systems that meet enterprise performance, security, and compliance requirements. • Conduct model evaluations, benchmarking, and performance testing. • Advise customers on model selection strategies and deployment architectures. • Support fine-tuning methodologies, including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Fine-Tuning (RFT). • Develop evaluation frameworks to measure model quality and business impact. • Design scalable inference architectures that support enterprise workloads. • Work with GPU infrastructure, containerized applications, Kubernetes, and cloud platforms. • Collaborate with customer engineering teams to optimize system reliability, latency, scalability, and performance. • Address infrastructure, security, and compliance challenges to ensure successful production deployments. • Present technical recommendations to engineering teams and executive leadership. • Build trusted relationships with customer stakeholders, identify champions, address objections, and drive successful deployments. • Identify recurring customer pain points and provide actionable feedback to internal product and engineering teams. • Influence product roadmap decisions through customer insights and field experience.