AI Lead

Tekskills; Gurugram District; On-site; INR 4,200,000 - 8,000,000; Full time

Job summary

Tekskills is seeking an AI Lead to architect GenAI and agentic systems that power enterprise workflows. You will drive end-to-end solution design, orchestration, and production deployment, blending backend engineering with AI/ML excellence.

You will lead cross-functional teams, define standards for prompt engineering, tool-calling, and RAG pipelines, and mentor engineers while collaborating with product and customers to deliver scalable AI platforms.

Qualifications

• 6+ years in software/solution architecture or backend engineering.
• 2+ years hands-on building GenAI/AgenticAI/LLM systems.

Responsibilities

• Design end-to-end GenAI architectures: multi-agent workflows, tool orchestration, memory systems, RAG pipelines.
• Develop Python/FastAPI microservices and serverless components with containerized workloads on Kubernetes.
• Architect retrieval pipelines using vector databases and design embedding, indexing, and hybrid retrieval strategies.

Skills

GenAI architecture

Backend engineering

Python

AWS

FastAPI

Kubernetes

Docker

LLM systems

Tools

Terraform

CloudFormation

ARM

Git

Job description

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 modern 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.