Role Overview

As an AI Lead, you will architect, build, and deliver cutting-edge GenAI and agentic systems that power enterprise and product workflows. This role blends deep technical expertise in backend engineering, cloud-native development, and contemporary AI/ML with the ability to design scalable AI architectures, lead engineering execution, and collaborate cross-functionally with product and business stakeholders. You will drive end-to-end solution design from ingestion, orchestration, and retrieval to model selection, evaluation, and production deployment.

Key Responsibilities
GenAI & Agentic Architecture Design
Design end-to-end GenAI architectures: multi-agent workflows, tool orchestration, memory systems, RAG pipelines, and long-running workflows.
Translate business problems into technical AI solution blueprints (models, tools, data flows, integrations).
Define standards and best practices for:
Prompt engineering
Tool-calling architecture
Context management
Retrieval strategies
Multi-agent orchestration
Backend & Microservices Development




Implement Python/FastAPI microservices and serverless components (AWS Lambda, Azure Functions), with containerized workloads on Kubernetes (EKS/AKS/GKE).
Define API contracts and integration patterns between agents, microservices, and external systems.
Develop ETL/ELT pipelines using Python/SQL for structured & unstructured data across data lakes/warehouses (S3, ADLS, Cosmos DB, BigQuery, SQL DB).
Ensure services meet non-functional requirements: scalability, performance, latency, and cost efficiency.
Retrieval & Knowledge Systems
Architect retrieval pipelines using vector databases: Qdrant, Weaviate, Chroma, PGVector.
Design embedding, indexing, chunking, and hybrid retrieval strategies.
Optimize RAG flows for enterprise-grade reliability and accuracy.
Evaluation, Governance & Safety
Establish evaluation frameworks: offline/online tests, A/B experiments, human-in-the-loop feedback loop

📌 Ai Lead Gurugram
🏢 Tekskills
📍 Gurugram