AI Engineer / Cloud Solutions Architect / LLM & Automation Specialist

Overview

We’re seeking an experienced AI Engineer / Cloud Solutions Architect / LLM & Automation Specialist with 8–10 years of overall IT experience, including 5+ years in AI/ML and 3+ years in GenAI & LLM-based system development.

This role is ideal for someone who thrives at the intersection of AI innovation, cloud architecture, and intelligent automation, and is passionate about bringing cutting-edge AI solutions into real-world enterprise environments.

As part of our advanced engineering team, you will design, architect, and deliver scalable AI agents, multi-agent systems, automation frameworks, and cloud-native AI applications that drive measurable business impact.

Key Responsibilities

AI & LLM Engineering

• Architect, develop, and deploy intelligent solutions using Large Language Models (LLMs) including Azure OpenAI, Google Gemini, and custom fine tuned models.
• Build production-grade AI agents, multi-agent orchestrations, and intelligent tool-augmented workflows.
• Develop context-aware pipelines using frameworks such as LangChain, LangGraph, Autogen, CrewAI, and Model Context Protocol (MCP).
• Design prompt engineering strategies, retrieval-augmented generation (RAG), vector search, and LLM evaluation frameworks.

Cloud Architecture & Engineering

• Architect cloud-native AI deployments on Azure (Functions, App Service, Kubernetes, Databricks, SQL, DevOps CI/CD).
• Design scalable, secure, and cost-optimized cloud solutions aligned with enterprise standards.
• Build containerized AI services using Docker, Kubernetes/KaaS, and distributed compute environments.
• Collaborate with security and infrastructure teams to integrate AI solutions with identity, access, and governance requirements.

Automation & Workflow Orchestration

• Create end-to-end automation frameworks powered by AI agents, APIs, and cloud services.
• Build intelligent workflows and prototypes using Microsoft Copilot Studio, Streamlit, and other low code environments.
• Integrate LLM capabilities into business processes, enterprise applications, and data pipelines.

End-to-End Delivery

• Drive AI solution lifecycle from experimentation, PoC, MVP through production hardening and monitoring.
• Perform model evaluations, feasibility studies, scalability planning, and cost estimation.
• Work cross-functionally with product, engineering, and business stakeholders to translate problems into intelligent solutions.
• Mentor teams and contribute to AI best practices, architecture patterns, and reusable components.

Core Skills & Technical Expertise

AI, ML & GenAI

• 5+ years’ experience in AI/ML including NLP, deep learning, embeddings, model tuning.
• 3+ years hands on experience with GenAI & LLM ecosystems.

Expertise in:

• Azure OpenAI (GPT 4, GPT 4o, custom models)
• Google Gemini, Claude (optional exposure)
• LangChain, LangGraph, Autogen, CrewAI, MCP
• RAG pipelines, vector stores (FAISS, Chroma, Azure AI Search)

Programming & Prototyping

• Strong expertise in Python (core libraries, async programming, API integration).
• Fast prototyping using Streamlit, Microsoft Copilot Studio, AI Foundry and REST API frameworks.

Cloud & DevOps

• Deep knowledge of Azure services: Functions, App Services, DevOps, Kubernetes, Databricks, Key Vault, Storage.
• Experience deploying scalable AI microservices, containerized apps, and secure API endpoints.
• Understanding of MLOps/AIOps frameworks, CI/CD pipelines, and observability patterns.

Automation & Integration

• Experience building agentic workflows, orchestration frameworks, and enterprise automation systems.
• Ability to integrate AI with enterprise apps, databases, APIs, and business workflows.

Preferred Qualifications

• Bachelor’s or master’s degree in computer science, Engineering, AI, or related field.
• Certifications in Azure or equivalent cloud/AI credentials.
• Experience with multi-agent system design, retrieval frameworks, and enterprise RAG.
• Exposure to security, governance, and responsible AI practices.