Note : If screened-in, you will be invited for initial rounds on 21st November 2026 (Saturday) in Hyderabad.

JOB SUMMARY :

Lead : 7+ years in software/ML engineering, with 3+ years leading development and production deployments of complex distributed or AI systems.

The Lead AI Engineer carries two connected mandates: building the agentic AI platform and deploying it into client environments. You will work alongside the Technical Architect and EM to translate architecture into working code and verified deployments.

KEY RESPONSIBILITIES :

1. Agentic AI Platform Bring Up :

- Multi-agent runtime deployment: Configure tool-call allowlists, agent registry, and validate agent topology.

- LLM inference and gateway configuration: Size GPU node pools, configure LiteLLM gateway, and manage rate limits.

- RAG and vector store bring-up: Stand up vector stores, manage per-tenant collections, and validate embedding pipelines.

- Workflow orchestration bring-up: Configure graph databases and workflow orchestrators.

2. Technical Expertise:

- Event-driven architecture: Kafka topology and Flink stateful stream processing.

- Graph database architecture: Neo4j causal cluster topology and Cypher performance optimization.

- Distributed workflow orchestration: LangGraph StateGraph design and dynamic DAG injection.

- Kubernetes platform architecture: Cluster topology for heterogeneous workloads and service mesh configuration.

- Cloud-agnostic IaC architecture: Terraform module hierarchy and zero-drift enforcement.

- Observability architecture: Prometheus metric design and Grafana dashboard governance.

3. Client-Facing Technical Leadership :

- Client stakeholder management: Act as the technical face of the deployment to client IT and security teams.

- Deployment runbook authorship: Maintain environment-specific runbooks for client operations.

- Knowledge transfer and enablement: Train client engineers for day-2 operations.

- Cross-team coordination: Sequence deployments across internal and client teams.

PREFERRED QUALIFICATIONS :

- 7+ years of professional experience in software or ML engineering, with at least 3 years leading deployment of complex distributed or AI systems.

- Direct experience deploying Kubernetes-based platforms into enterprise settings.

- Hands-on familiarity with agentic AI stacks (graph databases, vector stores, event streaming, GPU-served inference).

- Bachelor's or Master's degree in Computer Science or related technical discipline.

- Relevant certifications (CKA, CKAD, AWS/GCP Architect) are a plus.