Senior Consultant - AI Developer

<div class="content-intro"><p><span data-teams="true">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.</span></p></div><p><strong>Job Title: Senior Consultant – AI Application Engineer</strong></p> <p><strong>Work Location/Travel: Bangalore/Remote</strong></p> <p><strong></strong></p> <h2>Role Summary</h2> <p>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.</p> <hr> <h2>Key Duties and Responsibilities</h2> <h3>1. AI Solution Architecture</h3> <ul> <li>Design end-to-end AI application architecture for internal and client-facing use cases</li> <li>Define patterns for prompt orchestration, agent workflows, retrieval-augmented generation (RAG), tool calling, and enterprise integrations</li> <li>Select appropriate models, frameworks, vector stores, and deployment patterns based on cost, performance, and security considerations</li> <li>Establish reusable design patterns for future AI accelerators and company-owned IP</li> </ul> <h3>2. Product and Feature Development</h3> <ul> <li>Build production-grade AI applications, copilots, assistants, and workflow automations</li> <li>Lead development of reusable AI components that can be scaled across multiple engagements</li> <li>Translate roadmap initiatives into technical implementation plans, milestones, and deliverables</li> <li>Partner with product and business stakeholders to refine use cases into buildable solutions</li> </ul> <h3>3. LLM and GenAI Engineering</h3> <ul> <li>Evaluate and implement commercial LLMs through APIs and enterprise tooling</li> <li>Develop robust prompt strategies, context handling logic, tool usage patterns, and fallback mechanisms</li> <li>Design and optimize RAG pipelines using structured and unstructured enterprise knowledge sources</li> <li>Improve output quality, reliability, and usability of AI applications through testing and iteration</li> </ul> <h3>4. Engineering Standards and Production Readiness</h3> <ul> <li>Define coding standards, deployment standards, logging, monitoring, guardrails, and evaluation practices for AI applications</li> <li>Implement mechanisms for observability, tracing, prompt versioning, and response quality review</li> <li>Ensure solutions are secure, maintainable, scalable, and aligned with enterprise architecture principles</li> <li>Guide non-functional requirements including latency, reliability, token usage, and cost optimization</li> </ul> <h3>5. Technical Leadership</h3> <ul> <li>Serve as the technical lead for AI engineering efforts</li> <li>Mentor and guide the AI Developer / GenAI Engineer</li> <li>Support technical decision-making, effort estimation, and feasibility assessments</li> <li>Collaborate with cross-functional teams including product, architecture, delivery, QA, and operations</li> </ul> <h3>6. Stakeholder Collaboration</h3> <ul> <li>Participate in discovery sessions with business and delivery teams to identify opportunities for AI enablement</li> <li>Work with consulting, sales, and solution engineering teams to understand repeatable use cases</li> <li>Support demos, pilots, proofs of concept, and internal enablement where required</li> </ul> <h3>7. Evaluation and Continuous Improvement</h3> <ul> <li>Define testing and evaluation methods for AI outputs, workflows, and workflows involving enterprise data</li> <li>Improve system quality through prompt tuning, retrieval tuning, workflow redesign, model selection, and structured feedback loops</li> <li>Contribute to AI roadmap recommendations from a technical feasibility and maturity perspective</li> </ul> <hr> <h2>Skills / Profile to Look For</h2> <h3>Must-have</h3> <ul> <li>Strong software engineering background</li> <li>Experience building AI/LLM-powered applications</li> <li>Experience with APIs for OpenAI / Azure OpenAI / Anthropic / Google or similar</li> <li>Experience with Python and/or Node.js</li> <li>Experience with RAG, vector databases, embeddings, chunking, retrieval strategies</li> <li>Experience with orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent)</li> <li>Strong knowledge of cloud architecture and secure integrations</li> <li>Experience with prompt engineering, evaluation, and AI application debugging</li> <li>Ability to design scalable reusable systems</li> </ul> <h3>Good to have</h3> <ul> <li>Experience with enterprise SaaS ecosystems such as Salesforce / Oracle / Microsoft</li> <li>Experience with agentic workflows</li> <li>Experience with observability/evaluation platforms</li> <li>Experience working in consulting or product-based delivery organizations</li> <li>Exposure to AI governance, data privacy, and model risk considerations</li> </ul> <hr> <h2>What success looks like in first 6 months</h2> <ul> <li>Establishes the baseline architecture for AI applications</li> <li>Builds first reusable accelerator(s)</li> <li>Defines engineering standards for GenAI delivery</li> <li>Enables fast prototyping with production-minded design</li> <li>Acts as technical backbone for roadmap execution</li> </ul> <p><strong></strong></p>