Hyderabad, Telangana
Job Summary
Role Summary
The AI Technical / Engineering Manager is a senior, hands-on leadership role responsible for delivering complex AI and Generative AI programs from architecture through production at enterprise scale. This role combines deep technical expertise in AI systems with delivery leadership, ensuring that AI solutions are engineered correctly, operate reliably, and deliver measurable business outcomes. Unlike traditional delivery managers, this role remains technically accountable for AI design quality, non-functional requirements, and production readiness.
Key Responsibilities
Core Responsibilities
- Own end-to-end delivery of AI and GenAI programs, from solution design to production rollout and steady-state operations.
- Provide technical leadership across AI Engineers, Fullstack Engineers, LLMOps, Observability, QA, and RAI roles.
- Ensure AI system architectures meet scalability, reliability, security, observability, and cost targets.
- Actively participate in architectural decisions for RAG pipelines, agent-based systems, model selection, and data strategies.
- Drive engineering excellence through code reviews, design reviews, and technical governance forums.
- Establish delivery plans, sprint structures, and release strategies tailored for AI systems.
- Track technical KPIs including AI quality metrics, latency, cost, availability, and incident rates.
- Lead incident triaging, RCA, and continuous improvement for AI production systems.
- Ensure Responsible AI, security, and compliance controls are embedded into delivery workflows.
Skill Requirements
AI System Engineering Expertise
- Deep understanding of AI and Generative AI system design, including LLMs, embeddings, vector search, and retrieval pipelines.
- Strong experience with Retrieval-Augmented Generation (RAG) architectures and grounding strategies.
- Knowledge of agentic AI patterns including planners, tool/function calling, memory, and workflow orchestration.
- Experience operating distributed AI systems with strict latency, cost, and reliability requirements.
- Practical understanding of AI observability, drift detection, and quality evaluation techniques.
- Familiarity with Responsible AI risks, guardrails, human-in-the-loop patterns, and safety controls.
Tools & Technology Stack
- Programming languages: Python (mandatory), Java or equivalent backend languages.
- LLM platforms: Azure OpenAI, OpenAI, Anthropic, Google Vertex AI, or equivalent.
- AI frameworks: LangChain, LlamaIndex, LangGraph, or similar orchestration frameworks.
- Vector databases: Pinecone, FAISS, Weaviate, Azure AI Search.
- Cloud platforms: Azure (preferred), AWS, GCP.
- Platform & Ops: Docker, Kubernetes, CI/CD pipelines, infrastructure-as-code.
- Observability: OpenTelemetry concepts, dashboards, AI-specific telemetry.
Other Requirements
Leadership & Delivery Accountability
The AI Technical / Engineering Manager is accountable for engineering outcomes, not just project milestones. This role ensures teams build the right AI solutions correctly, scale them safely, and operate them reliably.
Experience & Qualifications
- 16+ years of experience in software engineering, architecture, or platform roles, with leadership responsibility.
- 4–6+ years delivering AI / ML / Generative AI systems in production environments.
- Proven experience leading multi-disciplinary engineering teams delivering enterprise-scale AI solutions
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