AI Engineering Lead

Job description

**AI Engineering Lead AI Platform & Agent Systems** **Location:** Bangalore, India **Experience:** 3 to 10 Years **Type:** Full-Time **Role Overview** We are building a next-generation **Enterprise AI Platform** powering **AI Agents, Multi-Agent Systems, RAG Applications, AI Workflows, and Knowledge Platforms**. We are looking for an **AI Engineering Lead** who will drive the design, architecture, and productionization of AI systems at scale. This is a **hands-on leadership role** responsible for owning the end-to-end lifecycle of AI infrastructure and applicationsfrom design and development to deployment, operations, and continuous optimization. You will lead the **AgentOps and AI Platform engineering efforts**, ensuring that AI systems are scalable, reliable, secure, and enterprise-ready across cloud and hybrid environments. **Key Responsibilities** **AI Platform & Architecture Leadership** - Lead the architecture and development of **AI platforms supporting agents, workflows, RAG systems, and LLM-based applications**. - Define best practices for **AI system design, model orchestration, inference pipelines, and runtime infrastructure**. - Drive the evolution of **AgentOps frameworks** for managing AI agents at scale. **Engineering Leadership** - Provide technical leadership and mentorship to a team of engineers working on AI infrastructure and platform systems. - Collaborate cross-functionally with **AI Research, Product, and Platform teams** to deliver production-grade AI solutions. - Establish engineering standards, design patterns, and development practices. **AI Systems & AgentOps** - Design and manage **AI agent architectures**, workflow orchestration, and multi-agent systems. - Build and operate **Model Gateways and LLM routing layers** across providers (OpenAI, Azure OpenAI, Claude, Gemini, etc.). - Lead development of **RAG systems** with vector databases and retrieval pipelines. - Optimize **latency, throughput, and cost** of AI workloads. **Platform & Infrastructure Engineering** - Own Kubernetes-based platforms for scalable AI workloads (GKE preferred). - Design and implement **cloud-native architectures** across GCP (primary), AWS/Azure (secondary). - Lead **Infrastructure-as-Code (Terraform)** and platform automation initiatives. - Establish **CI/CD and GitOps-based deployment models** for AI systems. **Reliability, Observability & Operations** - Define and implement **SRE practices** including monitoring, alerting, and incident management. - Architect observability using **OpenTelemetry, Prometheus, Grafana, ELK, or Cloud Monitoring**. - Drive **production readiness, scalability planning, and disaster recovery strategies**. **Enterprise Deployments & Security** - Ensure AI platform compliance with **enterprise-grade security and governance standards**. - Implement **IAM, RBAC, SSO, secrets management, and network security** controls. - Support **customer deployments across cloud, hybrid, and on-prem environments**. **Mandatory Skills & Experience** **AI Engineering & Systems** - Strong experience building and deploying **LLM-based applications and AI platforms**. - Deep understanding of **RAG architectures, vector databases, and AI inference pipelines**. - Experience with **AI model APIs and/or self-hosted inference (vLLM, TGI, Ollama, etc.)**. - Knowledge of **agent-based systems, workflow engines, and model orchestration patterns**. **Cloud & Platform Engineering** - Strong hands-on experience with **Google Cloud Platform (GCP)**. - Expertise in services such as **GKE, Cloud Run, BigQuery, Pub/Sub, IAM, VPC, and Monitoring**. - Experience designing **scalable, secure, multi-environment cloud architectures**. **Kubernetes & Distributed Systems** - Deep production experience with **Kubernetes (GKE preferred)**. - Strong understanding of **containerization, autoscaling, networking, storage, and high availability**. - Experience with **distributed systems and event-driven architectures**. **Infrastructure & DevOps** - Strong expertise in **Terraform and Infrastructure-as-Code frameworks**. - Experience with **CI/CD, GitOps (ArgoCD, Flux), and release automation**. - Proven ability to build **automated, scalable platform infrastructure**. **Observability & Reliability** - Experience with **monitoring, logging, and tracing systems** (Prometheus, Grafana, OTEL, etc.). - Strong understanding of **SRE principles, incident management, and reliability engineering**. **Leadership & Ownership** - Proven experience leading complex engineering initiatives or mentoring teams. - Ability to own **end-to-end system delivery and operational excellence**. **Highly Desirable** - Experience with **self-hosted LLM infrastructure** (vLLM, Ray Serve, TGI). - Hands-on with **vector databases** (Pinecone, Qdrant, Weaviate, pgvector). - Knowledge of **Service Mesh (Istio, Linkerd)**. - Experience with **workflow orchestration tools** (Temporal, Airflow, Dagster). - Exposure to **Platform Engineering / Internal Developer Platforms (IDP)**. - Understanding of **FinOps and cost optimization for AI workloads**.**Preferred candidate profile** **Preferred candidate profile** **Perks and benefits**