Senior Consultant Specialist

Own end-to-end delivery of AI-enabled applications, from initial discovery and solution scoping through prototype, production implementation, deployment, and adoption support. Partner with business stakeholders to understand operational workflows, pain points, acceptance criteria, and measurable success outcomes. Translate business requirements into practical AI / automation workflows, including LLM-powered features, RAG-based knowledge solutions, agentic workflows, APIs, and user-facing tools where appropriate. Build production-grade software using technologies such as Python, JavaScript / TypeScript, backend services, APIs, databases, and modern web or integration frameworks. Integrate AI / ML capabilities into enterprise applications, including prompt orchestration, retrieval pipelines, model APIs, evaluation logic, monitoring, and guardrails. Collaborate with product managers, AI researchers, data scientists, platform engineers, cyber security, risk, compliance, and operations teams to ensure solutions are fit for enterprise deployment. Design for reliability, maintainability, scalability, security, auditability, and responsible AI use in a regulated banking environment. Support deployment activities including CI/CD, environment configuration, observability, performance tuning, incident troubleshooting, and production readiness reviews. Gather feedback from real users and production telemetry to iterate on solution quality, model behavior, workflow usability, and business value. Act as a technical thought partner to business teams, helping them understand AI capabilities, limitations, trade-offs, and responsible adoption patterns. Bachelor's degree or above in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Software Engineering, or a related technical discipline, or equivalent practical experience. Around 5+ years of hands-on experience in AI engineering, LLM application engineering, machine learning engineering, data-intensive software engineering, or full-stack engineering with meaningful AI delivery exposure. Strong practical understanding of modern AI / ML and LLM application architectures, including RAG, embeddings, prompt engineering, model APIs, AI workflow orchestration, evaluation, monitoring, and guardrails. Hands-on experience building AI-powered applications or platforms, such as enterprise search, document intelligence, chatbot / assistant solutions, Text-to-SQL, agentic workflows, knowledge management, or workflow automation. Strong programming capability in Python, with the ability to build reliable AI pipelines, backend services, integrations, evaluation scripts, and automation tools. Solid software engineering capability in at least one additional application development language or ecosystem such as JavaScript / TypeScript, Java, or similar, with experience building APIs, services, UI components, or enterprise integrations. Proven experience moving AI or data-driven solutions beyond proof of concept into production or production-like environments, including testing, CI/CD, logging, monitoring, deployment, support, and iterative improvement. Ability to reason about AI system quality and trade-offs, including accuracy, hallucination risk, latency, cost, retrieval quality, data privacy, security, observability, maintainability, and user experience. Strong problem-solving and stakeholder communication skills, with the ability to translate business workflows into AI solution designs and explain technical trade-offs to both engineers and non-technical users. Strong ownership mindset, hands-on delivery attitude, and willingness to debug code, inspect data, tune prompts / retrieval logic, integrate systems, and unblock production delivery issues directly.