• Design, build, and deploy intelligent backend services using Python and Azure AI Foundry. • Consume Force Account outputs by API, ensuring the system can accurately display and act on data classification and extraction. • Engineer and optimize Retrieval-Augmented Generation (RAG) pipelines over Standard Operating Procedures (SOPs) to provide contextual, accurate, and actionable insights to users. • Deliver assistive intelligence directly on the case, seamlessly integrating AI outputs into the end-user's workflow. • Design and strictly enforce a redact-before-model discipline, ensuring that all Personally Identifiable Information (PII) is stripped from payloads before data ever reaches a foundational model. • Collaborate with product, design, and engineering teams to shape AI features and translate business goals into technical execution. • Proactively identify AI-driven automation and product improvement opportunities, building prototypes to prove value. • Mentor junior engineers and champion secure, responsible AI coding practices within your team. • Troubleshoot AI-specific technical challenges, including prompt injection risks, model latency, and data leakage. • Guide team members on model selection, evaluation, fine-tuning, and Gen AI usage. • Actively evaluate emerging LLM tooling, agentic frameworks, and open-source developments for applicability to client engagements. • Own the architecture of the team's agentic and LLM systems — agent orchestration, tool integration (e.g., Model Context Protocol), retrieval, and human-in-the-loop design. • Design and implement evaluation frameworks for LLM and agentic systems — covering correctness, boundary, and intent checks — as a standard part of delivery. • Drive adoption of the agentic SDLC across the team: select and roll out AI engineering tools, define usage and governance standards, and measure their impact on delivery velocity and quality. • Establish responsible-AI and evaluation frameworks for agentic systems — guardrails, safety testing, human oversight, and model governance.

• Design, build, and deploy intelligent backend services using Python and Azure AI Foundry. • Consume Force Account outputs by API, ensuring the system can accurately display and act on data classification and extraction. • Engineer and optimize Retrieval-Augmented Generation (RAG) pipelines over Standard Operating Procedures (SOPs) to provide contextual, accurate, and actionable insights to users. • Deliver assistive intelligence directly on the case, seamlessly integrating AI outputs into the end-user's workflow. • Design and strictly enforce a redact-before-model discipline, ensuring that all Personally Identifiable Information (PII) is stripped from payloads before data ever reaches a foundational model. • Collaborate with product, design, and engineering teams to shape AI features and translate business goals into technical execution. • Proactively identify AI-driven automation and product improvement opportunities, building prototypes to prove value. • Mentor junior engineers and champion secure, responsible AI coding practices within your team. • Troubleshoot AI-specific technical challenges, including prompt injection risks, model latency, and data leakage. • Guide team members on model selection, evaluation, fine-tuning, and Gen AI usage. • Actively evaluate emerging LLM tooling, agentic frameworks, and open-source developments for applicability to client engagements. • Own the architecture of the team's agentic and LLM systems — agent orchestration, tool integration (e.g., Model Context Protocol), retrieval, and human-in-the-loop design. • Design and implement evaluation frameworks for LLM and agentic systems — covering correctness, boundary, and intent checks — as a standard part of delivery. • Drive adoption of the agentic SDLC across the team: select and roll out AI engineering tools, define usage and governance standards, and measure their impact on delivery velocity and quality. • Establish responsible-AI and evaluation frameworks for agentic systems — guardrails, safety testing, human oversight, and model governance.

• Design, build, and deploy intelligent backend services using Python and Azure AI Foundry. • Consume Force Account outputs by API, ensuring the system can accurately display and act on data classification and extraction. • Engineer and optimize Retrieval-Augmented Generation (RAG) pipelines over Standard Operating Procedures (SOPs) to provide contextual, accurate, and actionable insights to users. • Deliver assistive intelligence directly on the case, seamlessly integrating AI outputs into the end-user's workflow. • Design and strictly enforce a redact-before-model discipline, ensuring that all Personally Identifiable Information (PII) is stripped from payloads before data ever reaches a foundational model. • Collaborate with product, design, and engineering teams to shape AI features and translate business goals into technical execution. • Proactively identify AI-driven automation and product improvement opportunities, building prototypes to prove value. • Mentor junior engineers and champion secure, responsible AI coding practices within your team. • Troubleshoot AI-specific technical challenges, including prompt injection risks, model latency, and data leakage. • Guide team members on model selection, evaluation, fine-tuning, and Gen AI usage. • Actively evaluate emerging LLM tooling, agentic frameworks, and open-source developments for applicability to client engagements. • Own the architecture of the team's agentic and LLM systems — agent orchestration, tool integration (e.g., Model Context Protocol), retrieval, and human-in-the-loop design. • Design and implement evaluation frameworks for LLM and agentic systems — covering correctness, boundary, and intent checks — as a standard part of delivery. • Drive adoption of the agentic SDLC across the team: select and roll out AI engineering tools, define usage and governance standards, and measure their impact on delivery velocity and quality. • Establish responsible-AI and evaluation frameworks for agentic systems — guardrails, safety testing, human oversight, and model governance.

• Lead, mentor, and develop a high-performing Solution Engineering team in a fast-paced, high-growth environment. • Recruit, onboard, and retain top Solution Engineering talent in line with business growth plans. • Develop and execute a scalable Solution Engineering strategy across Germany and Continental Europe. • Allocate resources effectively across territories to support business objectives and maximise impact. • Provide ongoing coaching, performance management, and meaningful feedback to team members. • Act as an active Solution Engineering leader, partnering with the team on strategic opportunities and customer engagements. • Support revenue growth by demonstrating business value, delivering compelling product demonstrations, and helping customers envision future success with Freshworks. • Build scalable and repeatable frameworks, programmes, and best practices to support growth across the region. • Partner closely with Sales Leadership to improve pipeline quality, forecast accuracy, and overall win rates. • Collaborate with Product Management and Product Marketing teams to influence product direction and advocate for customer requirements. • Serve as a trusted advisor to internal and external stakeholders, including C-level executives, providing both business and technical insights. • Develop competitive strategies and enablement programmes that strengthen our positioning in the market.