Senior Consultant - AI Developer

Apex IT is a global consulting firm that provides award-winning services to transform the customer, employee, and student experiences. Since 1997, Apex IT our Salesforce and Oracle experts have provided a full range of enterprise solutions including CRM and related applications that support sales, marketing, and service; financial reporting; HR; and Business Intelligence. As a remote company, we have top talent all over the United States and India and are continuously growing. We provide our team with a flexible work-life balance in addition to the traditional benefits. Job Title: Senior Consultant – AI Application Engineer Work Location/Travel: Bangalore/Remote Role Summary The Senior AI Application Engineer will design and build reusable AI-powered applications and accelerators that support internal operations, consulting delivery, and client-facing innovation. This role will be responsible for translating business and product requirements into scalable technical solutions using commercial language models, orchestration frameworks, retrieval systems, and enterprise integrations. Key Duties and Responsibilities 1. AI Solution Architecture Design end-to-end AI application architecture for internal and client-facing use cases Define patterns for prompt orchestration, agent workflows, retrieval-augmented generation (RAG), tool calling, and enterprise integrations Select appropriate models, frameworks, vector stores, and deployment patterns based on cost, performance, and security considerations Establish reusable design patterns for future AI accelerators and company-owned IP 2. Product and Feature Development Build production-grade AI applications, copilots, assistants, and workflow automations Lead development of reusable AI components that can be scaled across multiple engagements Translate roadmap initiatives into technical implementation plans, milestones, and deliverables Partner with product and business stakeholders to refine use cases into buildable solutions 3. LLM and GenAI Engineering Evaluate and implement commercial LLMs through APIs and enterprise tooling Develop robust prompt strategies, context handling logic, tool usage patterns, and fallback mechanisms Design and optimize RAG pipelines using structured and unstructured enterprise knowledge sources Improve output quality, reliability, and usability of AI applications through testing and iteration 4. Engineering Standards and Production Readiness Define coding standards, deployment standards, logging, monitoring, guardrails, and evaluation practices for AI applications Implement mechanisms for observability, tracing, prompt versioning, and response quality review Ensure solutions are secure, maintainable, scalable, and aligned with enterprise architecture principles Guide non-functional requirements including latency, reliability, token usage, and cost optimization 5. Technical Leadership Serve as the technical lead for AI…