Lead AI/ML Engineer - R01572495

Brillio; Bengaluru; On-site; INR 2,800,000 - 5,200,000; Full time

Job summary

Brillio in Bengaluru invites an experienced Lead AI/ML Engineer to design and deploy enterprise-scale AI platforms focusing on agentic AI, LLMs, and Azure-based solutions. You will implement end-to-end AI agents, RAG pipelines, and seamless integration with Microsoft 365 and enterprise APIs.

You will build production-grade AI applications with memory, orchestration, and robust security, while ensuring observability and governance in live environments.

Qualifications

• 4–6 years designing, building and operationalizing enterprise AI platforms.
• Hands-on expertise in agentic AI, LLMs and Azure-based solutions.
• Strong Python and ML framework experience.

Responsibilities

• Design, build, and deploy AI agents and multi-agent systems on Azure.
• Develop agentic workflows integrated with Microsoft 365 and enterprise APIs.
• Implement RAG pipelines and vector search for knowledge retrieval.
• Build production-grade AI apps with memory, orchestration, and security.
• Establish observability, guardrails and governance for AI in production.

Skills

Azure AI services

Agentic frameworks

LLM development

RAG architectures

Microsoft 365 integration

Python programming

ML frameworks

Workflow orchestration

Observability & security

Education

Bachelor's degree in CS/Data Science/Maths

Certifications in ML/DS (e.g., Azure AI Engineer)

Certifications in statistical tools (SAS/SPSS)

Tools

Semantic Kernel

LangChain

LangGraph

AutoGen

Azure OpenAI

Azure AI Foundry

Azure Functions

AKS

KubeFlow

BentoML

Evidently AI

Great Expectations

Azure AI Search

Vector search

Power Platform

Job description

Lead AI/ML Engineer

Job requirements

Experience Range: With at least 4 to 6 years of experience designing, building, and operationalizing enterprise-scale AI platforms, including hands‑on expertise in agentic AI, LLMs, and Azure-based solutions.

Key Responsibilities

• Design, build, and deploy AI agents and multi‑agent systems using Azure AI services, Azure OpenAI, and modern agentic frameworks such as Semantic Kernel, LangChain, LangGraph, and AutoGen.
• Develop and operationalize agentic workflows for business functions, integrating AI solutions with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and enterprise APIs.
• Implement enterprise RAG solutions utilizing Azure AI Search, vector search, hybrid search, and semantic ranking to enhance knowledge retrieval and business decision‑making.
• Build production‑ready AI applications leveraging LLMs, RAG pipelines, tool/function calling, memory systems, and workflow orchestration for scalable, secure, and reliable enterprise deployment.
• Establish observability, guardrails, responsible AI controls, evaluation, security, and monitoring for AI applications in production environments.
• Optimize agent and LLM applications for latency, accuracy, reliability, scalability, security, and cost, applying advanced techniques and metrics.
• Collaborate with cross‑functional teams to identify AI opportunities, rapidly prototype solutions, and iterate based on user feedback.
• Contribute to internal best practices around agent architecture, prompting, RAG, model selection, evaluation, and AI engineering standards.

Required Skills

• Hands‑on experience with Azure AI services, Azure OpenAI, and Azure AI Foundry
• Expertise in agentic frameworks (Semantic Kernel, LangChain, LangGraph, AutoGen)
• LLM application development and prompt engineering
• Enterprise RAG architectures and vector search technologies
• Integration with Microsoft 365, Teams, SharePoint, Dynamics 365, Power Platform, and APIs
• Python programming for AI/ML solutions
• ML frameworks (TensorFlow, PyTorch, Sci‑Kit Learn, Keras)
• AI workflow orchestration tools (KubeFlow, BentoML)
• Model evaluation and observability tools (Evidently AI, Great Expectations)
• AI security and governance on Azure

Preferred Skills

• Experience with Azure Functions, Azure Container Apps, Azure Kubernetes Service (AKS), and Azure Storage
• Proficiency in R and R Studio for statistical computing
• Knowledge of SAS or SPSS for advanced statistical modeling
• Familiarity with microservices, event‑driven architectures, and enterprise integrations
• Expertise in developing reusable agent frameworks, prompt libraries, and deployment patterns

Desired Qualifications

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
• Certification in Machine Learning or Data Science from a recognized institution (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty, Microsoft Certified: Azure AI Engineer Associate)
• Certification in statistical analysis tools or platforms (e.g., SAS Certified Statistical Business Analyst)