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Senior Artificial Intelligence Engineer

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Onsite

Senior Artificial Intelligence Engineer

Coxvi Technologies

Senior Artificial Intelligence Engineer

Skills

Google Cloud PlatformDockerData ScienceMachine LearningAIAWSKubernetesn8nAzure

8 days ago

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Important information


Contract type:

Permanent

Salary:

₹3,000,000 – ₹4,500,000 / year

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

17 October 2026

What they need


Job Title: Senior AI Engineer Agentic Systems

Location: Bengaluru, Karnataka
Experience: 610 Years
Employment Type: Contract through March 2027, extendable

About the Organization

A4I Lab (AI Innovation and Inclusion Initiative) is a partnership between IIIT Bangalore and Microsoft. The initiative collaborates with non-profit partners to apply AI to real-world challenges in education, healthcare, accessibility and agriculture.

The initiative focuses on developing open-source Digital Public Goods (DPGs) that can be deployed at scale for meaningful social impact.

Role Overview

We are looking for a Senior AI Engineer with strong hands-on experience designing and deploying production-grade agentic AI systems. The ideal candidate will architect multi-agent workflows, build scalable RAG pipelines, integrate multimodal AI capabilities and drive AI platform engineering.

This is a hands-on technical leadership role focused on building reliable AI systems for real-world users and resource-constrained environments.

Key Responsibilities

Agentic AI Systems

  • Design and build multi-agent orchestration pipelines for production environments.
  • Translate complex, multi-stakeholder use cases into resilient, auditable and deployable agentic workflows.
  • Own AI system components from research prototypes through production deployment.
  • Collaborate with researchers, domain partners and end users.

LLM, RAG and Multimodal AI

  • Build and maintain RAG pipelines, vector stores, MCP tools and graph databases.
  • Develop dynamic, multilingual knowledge retrieval systems.
  • Integrate speech, vision and document inputs into unified agent workflows.
  • Fine-tune and evaluate LLMs for domain-specific applications.
  • Develop evaluation frameworks to measure production performance and quality.

AI Platform Engineering and MLOps

  • Architect scalable cloud-based AI platforms.
  • Build reproducible ML pipelines using Docker, Kubernetes, CI/CD and model versioning.
  • Establish AI-Ops practices for faster model deployment and safe, monitored rollouts.
  • Define architectural standards across engineering, data science and research teams.

Technical Leadership

  • Mentor engineers and researchers across partner organizations.
  • Communicate architectural trade-offs to technical and non-technical stakeholders.
  • Contribute to open-source projects and documentation for reusable Digital Public Goods.

Mandatory Skills and Qualifications

  • 610 years of experience designing and building software systems.
  • At least 2 years of hands-on experience developing AI/ML applications in production.
  • At least one end-to-end agentic AI workload deployed in production with real users.
  • Hands-on experience with LangGraph, LlamaIndex, n8n, Potpie or comparable agent frameworks.
  • Strong understanding of multi-agent architecture, RAG, vector retrieval, caching, embeddings and context-window management.
  • Experience with at least two cloud platforms among AWS, GCP and Azure.
  • Experience with Docker, application lifecycle tooling and production deployments.
  • Ability to design systems supporting 10,000+ concurrent users with acceptable latency and cost.
  • Experience driving technical architecture and mentoring engineers.
  • Verifiable experience building and deploying large-scale AI platforms
  • .

Education

Bachelor's or Master's degree in Computer Science, Electrical Engineering or equivalent demonstrated expertise.

Preferred Qualifications

  • Experience with vision-language models (VLMs), speech models and multilingual or Indic-language AI pipelines.
  • Knowledge of AI services and open-source alternatives.
  • Experience delivering technology in social impact, public-sector or resource-constrained environments.
  • Familiarity with AI evaluation frameworks and production measurement.
  • Open-source AI contributions or publications in agent systems and applied AI.

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