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 engineering efforts

Mentor and guide the AI Developer / GenAI Engineer

Support technical decision-making, effort estimation, and feasibility assessments

Collaborate with cross-functional teams including product, architecture, delivery, QA, and operations

6. Stakeholder Collaboration

Participate in discovery sessions with business and delivery teams to identify opportunities for AI enablement

Work with consulting, sales, and solution engineering teams to understand repeatable use cases

Support demos, pilots, proofs of concept, and internal enablement where required

7. Evaluation and Continuous Improvement

Define testing and evaluation methods for AI outputs, workflows, and workflows involving enterprise data

Improve system quality through prompt tuning, retrieval tuning, workflow redesign, model selection, and structured feedback loops

Contribute to AI roadmap recommendations from a technical feasibility and maturity perspective

Skills / Profile to Look For

Must-have

Strong software engineering background

Experience building AI/LLM-powered applications

Experience with APIs for OpenAI / Azure OpenAI / Anthropic / Google or similar

Experience with Python and/or Node.js

Experience with RAG, vector databases, embeddings, chunking, retrieval strategies

Experience with orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or equivalent)

Strong knowledge of cloud architecture and secure integrations

Experience with prompt engineering, evaluation, and AI application debugging

Ability to design scalable reusable systems

Good to have

Experience with enterprise SaaS ecosystems such as Salesforce / Oracle / Microsoft

Experience with agentic workflows

Experience with observability/evaluation platforms

Experience working in consulting or product-based delivery organizations

Exposure to AI governance, data privacy, and model risk considerations

What success looks like in first 6 months

Establishes the baseline architecture for AI applications

Builds first reusable accelerator(s)

Defines engineering standards for GenAI delivery

Enables fast prototyping with production-minded design

Acts as technical backbone for roadmap execution