Key Responsibilities Design and implement scalable AI/ML and Generative AI architectures for enterprise applications.

Develop LLM-powered applications, autonomous AI agents, and multi-agent orchestration systems.

Architect state management and persistent memory systems for long-running AI workflows.

Build and optimize Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector databases.

Lead the evaluation, selection, and implementation of AI technologies, frameworks, and infrastructure.

Collaborate with stakeholders to define business requirements and convert them into technical solutions.

Design and implement AI evaluation, observability, and monitoring frameworks.

Develop scalable APIs, microservices, and distributed AI systems.

Deploy and manage AI solutions on cloud platforms, primarily AWS.

Implement MLOps/LLMOps practices for model lifecycle management and deployment automation.

Mentor engineering teams and bridge the gap between Data Science and ML Engineering teams.

Contribute to solution proposals, RFP responses, architecture documentation, and effort estimations.

Ensure adherence to industry best practices, security standards, and performance engineering principles.

Required Skills & Qualifications

AI & Machine Learning

Strong expertise in Machine Learning, Generative AI, and Large Language Models (LLMs).

Hands-on experience designing and deploying LLM-based applications and agentic AI systems.

Experience with multi-agent orchestration frameworks such as: LangGraph, CrewAI, AutoGen, Semantic Kernel, Strong understanding of: Prompt engineering, Embeddings,



Vector databases, RAG architecture, Autonomous workflow design

Experience implementing AI evaluation and monitoring frameworks.

Familiarity with ML pipelines and frameworks such as MLflow, Kubeflow, or similar platforms.

Strong programming expertise in Python. Hands-on experience with: NumPy, Pandas, Scikit-learn

Experience designing scalable microservices and distributed systems.

Strong API development and integration experience.

Cloud & Infrastructure

Experience deploying AI solutions on AWS.

Familiarity with Docker and Kubernetes.

Understanding of AI infrastructure, vector databases, and data pipelines.

Experience with MLOps and LLMOps platforms.

Architecture & Leadership

Expertise in distributed systems architecture.

Strong understanding of scalability, reliability, and performance engineering.

Ability to design enterprise-grade AI platforms and frameworks.

Robust technical leadership and mentoring capabilities.

Excellent analytical, communication, and stakeholder management skills.

Ability to explain complex AI concepts to both technical and non-technical audiences.

Strong documentation and architecture communication skills.

Preferred Qualifications

Years Of Experience: 14 to 18 Years

Education/Qualification: BE / B.Tech / MCA / M.Tech Experience working on enterprise AI transformation initiatives.

Exposure to autonomous AI systems and workflow orchestration platforms.

Experience contributing to RFPs, technical proposals, and solution estimations.

Proven track record of deploying AI solutions into production environments.

📌 Senior AI Architect (Bengaluru)
🏢 Impetus
📍 Bengaluru