Artificial Intelligence Developer
On-siteBengaluru, Karnataka, India
Job Summary
Design and deploy scalable AI applications on Azure infrastructure, integrating LLMs, RAG pipelines, and multi-turn conversational agents for internal tools and client-facing advisory solutions. Build and maintain local model deployments using Ollama or vLLM while applying responsible AI governance, including content filtering, PII detection, and audit logging. Optimize cost-conscious architectures by selecting models and deployment tiers that balance latency, security, and token consumption, supported by Azure Monitor instrumentation and regular cost reporting. Deliver production-grade features via CI/CD pipelines and infrastructure-as-code, collaborating with the Architecture team on design reviews and mentoring junior developers on AI patterns and Azure best practices.
Required Qualifications
- 3+ years of hands-on software development, with at least 2 years focused on AI/ML application development in a production environment
- Demonstrated experience building production applications with LLMs across multiple model families — OpenAI, Anthropic Claude, Meta Llama, Mistral, or similar
- Hands-on experience building RAG pipelines — document ingestion, embeddings, vector search, chunking strategies, and retrieval optimisation
- Experience standing up and operating local or self-hosted AI model deployments (Ollama, vLLM, LocalAI, or equivalent)
- Experience using Claude Code or equivalent AI-assisted development tooling as part of everyday engineering workflow
- Python proficiency — FastAPI or Flask, async patterns, and data processing libraries
- Hands-on experience with Azure AI services — Azure OpenAI Service, Azure AI Search, Azure AI Document Intelligence, and/or Azure AI Foundry
- Familiarity with orchestration frameworks — Semantic Kernel, LangChain, or LlamaIndex
- Security-first mindset — Managed Identity, Key Vault, RBAC, Private Endpoints, and authentication/authorisation patterns in cloud applications
- Cost-conscious approach to AI architecture — model selection, token optimisation, deployment tier decisions, and cost monitoring
- Excellent written and spoken English — able to communicate technical concepts and architectural decisions clearly to US-based stakeholders, produce accurate documentation, and participate confidently in cross-regional meetings
Desired Qualifications
- Azure AI certifications: AI-102 (Azure AI Engineer Associate) or AZ-204 (Azure Developer Associate)
- Experience with multi-tenant SaaS application architecture and data isolation patterns
- Experience with front-end frameworks (React or similar) for building AI-powered user interfaces
- Knowledge of Azure Data Lake Storage, Azure Synapse, or Azure Data Factory for AI data pipeline integration
- Exposure to responsible AI frameworks, content safety tooling, or compliance requirements in regulated industries
- Experience with infrastructure-as-code: Bicep or Terraform
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