Senior Consultant - AI Developer

Apex IT

IndiaremotePosted 11 days ago

Skill Required

AI-DeveloperAI-EngineerSenior-ConsultantMachine-Learning-EngineerGenerative-AI-EngineerAI-Engineer-Senior-ConsultantSenior-AI-DeveloperSenior-AI-Software-DeveloperSenior-Developer-ConsultantAI EngineerAIsoftware engineeringGenerative AIDesign PatternsObservabilityPrototypingEngineeringSalesforceLangChaindebuggingNode.jssecuritybuildingTestNGPythonOracledesignOpenAPIAzureCloudAPIsRAGandFulltime

Key highlights

Work Location/Travel: Bangalore/Remote
Experience building AI/LLM-powered applications
Flexible work-life balance
Experience with Python and/or Node.js

Role overview

Apex IT is a global consulting firm specializing in Salesforce and Oracle solutions for CRM, financial reporting, HR, and Business Intelligence. As a remote-first company, they offer flexible work-life balance and traditional benefits. The Senior AI Application Engineer will design and build reusable AI-powered applications and accelerators for internal operations, consulting delivery, and client-facing innovation.

Responsibilities

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
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
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
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
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
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
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

Requirements

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

Nice 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