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 up-to-date 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 loops.

- Implement guardrails: input/output safety, content filters, hallucination detection.

- Ensure compliance with data security, privacy, access control, and safe AI principles.

- Leadership, Collaboration &

- Delivery

- Serve as a technical leader across engineering squads-providing mentorship, code reviews, and design guidance.

- Partner with product managers, customers, and cross-functional teams to define requirements, scope, and architectural decisions.

- Lead architecture reviews, maintain decision records, and deliver technical documentation.

- Represent the AI team in customer meetings, proposals, and solution walkthroughs.

Required Skills &

- ExperienceTechnical Expertise

- 6+ years in software/solution architecture or backend engineering.

- 2+ years hands-on building GenAI/ AgenticAI/ LLM systems.

Strong proficiency in:

- Python (OOP, async, API development)

- Cloud (AWS required
- Azure/GCP is a plus)

- FastAPI, serverless functions (Lambda/Azure Functions)

- Docker, Kubernetes, container orchestration





Strong understanding of:

- LLMs, embeddings, prompt engineering

- Tool-calling, multi-agent patterns

- RAG design, vector search, chunking strategies

- Experience with vector databases (Qdrant, Weaviate, PGVector, Chroma)

- Solid knowledge of SQL &
- Python ETL for data engineering

Hands-on experience with:

- AWS services (S3, Lambda, API Gateway, EventBridge, DynamoDB/RDS)

- Azure services (ADLS, Cosmos DB, Azure SQL)

- CI/CD automation and Git workflows

- Fundamental front-end knowledge (React/Angular/HTML/CSS) for collaboration

Engineering Practices

- Strong unit/integration testing discipline.

- Experience with IaC (Terraform/CloudFormation/ARM).

- Strong understanding of cloud-native observability (logging, metrics, tracing).

- Experience running systems in Agile squads with Jira or similar tools.

Soft Skills &

- Leadership

- Excellent communication-capable of simplifying complex AI concepts.

- Strong ownership, product mindset, and customer-facing confidence.

- Ability to balance rapid experimentation with production reliability.

- Comfortable leading architecture discussions and mentoring engineers.

Preferred Qualifications

- Experience contributing to open-source projects or AI/ML communities.

- Cloud certifications (AWS Developer Associate, Azure Developer, GCP Cloud Developer) are plus.

- Experience with data governance, enterprise security, and compliance frameworks.

- Exposure to agentic AI platforms/ Frameworks (e.g., LangChain, langgraph, Autogen, Haystack, OpenAI's MCP etc).

- Contributions to advanced RAG systems or prompt engineering frameworks.

📌 AI Lead (Gurugram)
🏢 Tekskills
📍 Gurugram