AI Tech Lead/Architect

Role & responsibilities

Seniority

Lead Engineer / Senior Technical Lead / Engineering Lead

Experience Required

• 7 to 14+ years overall software engineering experience
• 4+ years in AI/ML or intelligent automation development
• 2+ years in GenAI, RAG, LLM applications, or agent-based systems
• Strong preference for hands-on experience in enterprise integration, platform engineering, AIOps, or network automation

Role Summary

We are seeking an AI Tech Lead to lead hands-on implementation of AI agents, RAG pipelines, tool integrations, APIs, orchestration workflows, and production-grade services for an autonomous network operations platform.

This role bridges architecture and delivery. The AI Tech Lead will convert the AI Architects blueprint into working services, reusable engineering patterns, agent workflows, integration components, and deployment-ready modules.

Key Responsibilities

• Lead the hands-on design, development, and implementation of AI agents, RAG services, workflow orchestration frameworks, and network automation integrations while mentoring engineering teams and ensuring adherence to architectural standards.
• Translate architecture into technical designs, sprint-level tasks, reusable components, and coding standards.
• Build and review production-grade services for AI orchestration, tool invocation, prompt workflows, data retrieval, and response grounding.
• Implement APIs and microservices for agent communication, network tool integration, telemetry lookup, incident enrichment, and remediation planning.
• Define coding standards for prompt templates, retrieval pipelines, function calling, agent state, error handling, retry logic, and observability.
• Lead integration with vector databases, model gateways, LLM providers, observability stacks, network controllers, ITSM, and configuration management systems.
• Implement secure tool execution patterns with authorization, validation, audit logs, and policy checks.
• Ensure system design quality for scalability, resilience, latency, reliability, and maintainability.
• Mentor AI Developers and ML Engineers.
• Collaborate with QA to define AI evaluation tests, regression suites, and release gates.
• Participate in incident reviews, performance tuning, model/prompt optimization, and production support.

Required Skills

• Strong hands-on experience in Python, API development, microservices, and backend engineering.
• Experience building LLM applications using RAG, embeddings, vector databases, prompt engineering, tool/function calling, and agent workflows.
• Experience with frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, LlamaIndex, or equivalent.
• Strong system design implementation knowledge, including distributed services, asynchronous processing, event-driven design, caching, retries, circuit breakers, and observability.
• Experience with FastAPI, Flask, Django, Node.js, or similar backend frameworks.
• Experience integrating with REST APIs, databases, event streams, log systems, telemetry systems, and enterprise platforms.
• Knowledge of containers, Kubernetes, CI/CD pipelines, Git workflows, and DevOps practices.
• Good understanding of AI safety controls such as hallucination reduction, source grounding, prompt injection protection, and response validation.

Preferred candidate profile

Immediate to 30 Days