AI Engineering Architect

Responsibilities

AI Architecture & Engineering

Define and own AI reference architectures for generative AI, agentic systems, and AI augmented applications

Architect scalable solutions using LLMs, multi agent systems, orchestration frameworks, and AI pipelines

Design AI platforms supporting model serving, prompt management, RAG, and workflow orchestration

Establish architectural standards for performance, scalability, reliability, and cost efficiency

Platform Engineering & Integration

Build reusable AI components for LLM integration, vector search, embeddings, and inference services

Enable secure and scalable deployment using Kubernetes, serverless platforms, and CI/CD pipelines

Integrate AI capabilities into enterprise systems using APIs, SDKs, and event driven architectures

Collaborate with QE teams to embed AI into test automation, test data generation, and intelligent validation

Engineering Governance & Quality

Define architectural guardrails for model lifecycle, versioning, monitoring, and rollback

Ensure adherence to non functional requirements including performance, observability, and fault tolerance

Leverage observability tools to monitor model performance and drift

Review designs and implementations for architectural compliance and code quality

Mentor engineers and architects on AI engineering best practices

Core Platforms, Frameworks & Tooling

LLM and foundation model platforms (e.g., AWS Bedrock, Azure OpenAI, Vertex AI)

Agentic AI and orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, Google ADK or equivalent)

Vector databases and search technologies (OpenSearch, Pinecone, FAISS, Weaviate)

Model lifecycle and deployment tooling (Kubernetes, containers, serverless runtimes)

CI/CD and MLOps tooling for AI pipelines (GitHub Actions, Azure DevOps, Jenkins)

Observability and monitoring tooling for AI systems (OpenTelemetry, Prometheus, Grafana)

Client Orientation & Leadership

Partner with product and engineering teams to identify AI opportunities and shape roadmaps

Support client workshops, RFPs, and solution presentations

Mentor engineers on AI/ML/Gen AI best practices and emerging technologies

Translate complex AI concepts into business-friendly narratives.

Technical requirements

13+ years of experience in software engineering with 3+ years in AI with strong architecture ownership

Proven experience designing and implementing enterprise-scale AI engineering or MLOps platforms

Strong hands on experience with LLMs, prompt engineering, RAG, and agent frameworks

Proficiency in Python, AI frameworks, and cloud-native AI services

Experience in Kubernetes, CI/CD, and secure deployment of AI models

Experience integrating AI capabilities into enterprise scale systems

Good to Have Skills

Experience with multi agent orchestration and autonomous workflows

Knowledge of model observability and monitoring tooling

Exposure to QE platforms, test automation frameworks, or AI assisted testing

Domain experience in regulated industries such as BFSI, Healthcare, Telecom

Cloud and AI certifications

Education

Bachelor of Engineering