Senior AI Technical Architect (AI-DLC / Agentic AI)

Overview

Senior AI Technical Architect

AI-Driven Development Lifecycle (AI-DLC)

Key Responsibilities

• Design and implement an end-to-end

AI-DLC (AI-Driven Development Lifecycle)

• framework across software engineering functions.
• Architect and deploy
• AI-powered solutions
• for software development, QA automation, testing, code generation, deployment, and monitoring.
• Design and build
• AI agents and Agentic AI systems
• for engineering workflows, automation, and intelligent decision-making.
• Evaluate, implement, and integrate
• LLMs, AI orchestration frameworks, vector databases, and AI services
• into enterprise systems.
• Define the technical architecture for
• AI-first software engineering ecosystems
• , ensuring scalability, security, and governance.
• Implement AI-based solutions for:
• Intelligent code generation and code review
• AI-assisted software testing and QA automation
• Deployment automation and DevOps optimization
• Incident detection, monitoring, and predictive maintenance
• Engineering productivity enhancement
• Work closely with engineering, product, DevOps, and leadership teams to define AI adoption strategies and implementation roadmaps.
• Establish best practices around
• MLOps, LLMOps, model governance, prompt engineering, and AI observability
• .
• Lead technical PoCs, evaluate AI tools/vendors, and recommend scalable enterprise-grade solutions.
• Mentor technical teams and enable organization-wide adoption of AI engineering practices.

Required Skills & Experience

• 10+ years of experience in

Software Architecture, Engineering, or Enterprise Technology Leadership

• .
• Proven hands-on experience implementing

AI-DLC / AI-enabled SDLC transformation

• from a technical perspective.
• Strong experience building and deploying

AI Agents / Agentic AI systems

• .
• Deep understanding of
• Generative AI, LLMs, Multi-Agent Systems, and AI orchestration frameworks
• .
• Experience with frameworks/tools such as:
• LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, LlamaIndex
• OpenAI, Anthropic, Azure AI, AWS Bedrock, Google Vertex AI

• Vector Databases (Pinecone, Weaviate, Chroma, FAISS)

• Strong experience in

Cloud Architecture

• (AWS, Azure, GCP).
• Experience integrating AI into:
• CI/CD pipelines
• DevOps and QA frameworks
• Engineering productivity workflows
• Knowledge of
• MLOps, LLMOps, Prompt Engineering, RAG architectures, and AI governance
• .
• Strong understanding of enterprise architecture, APIs, microservices, and scalable distributed systems.

Preferred Qualifications

• Experience implementing AI transformation within enterprise engineering teams.
• Exposure to AI-driven testing, autonomous engineering workflows, and intelligent DevOps.
• Prior consulting or transformation leadership experience is highly preferred.
• Bachelor's or Master's degree in Computer Science, Engineering, AI, or related field.