AI engineers - Advisor

We are currently seeking an AI Engineer - Advisor to join our team in Bangalore, Karnataka (IN-KA), India (IN).

• Design and develop AI and Generative AI applications using Large Language Models (LLMs) and modern AI frameworks.
• Build and integrate AI agents and agentic workflows capable of reasoning, tool calling, task execution, and interaction with enterprise systems.
• Develop prompt engineering, context management, structured-output, and function/tool-calling capabilities.
• Implement Retrieval-Augmented Generation (RAG) solutions using enterprise data sources, embeddings, semantic search, and vector databases.
• Develop reusable AI components, APIs, services, connectors, and accelerators.
• Integrate AI capabilities into web applications, enterprise platforms, workflows, and existing business systems.

• Integrate commercial and open-source LLMs using APIs and model-serving platforms.
• Develop single-agent and multi-agent workflows using appropriate orchestration frameworks.
• Implement agent tools, memory, state management, workflow orchestration, and human-in-the-loop capabilities.
• Implement mechanisms to improve grounding and reduce hallucinations and unreliable responses.
• Develop AI guardrails, input/output validation, content controls, and error-handling mechanisms.
• Optimize prompts, model parameters, retrieval strategies, and workflows for quality, latency, reliability, and cost.

• Build ingestion and indexing pipelines for structured and unstructured enterprise content.
• Implement document parsing, chunking, metadata enrichment, embedding generation, indexing, and retrieval.
• Develop semantic, keyword, and hybrid search capabilities.
• Integrate vector databases and enterprise search platforms.
• Optimize retrieval quality through query transformation, reranking, filtering, and contextual retrieval techniques.
• Implement source attribution and grounding mechanisms where required.

• Develop REST APIs, microservices, event-driven integrations, and backend services supporting AI applications.
• Integrate AI solutions with enterprise applications, databases, APIs, SaaS platforms, and external services.
• Implement authentication, authorization, secrets management, and secure API communication.
• Develop connectors and tools that enable AI agents to securely interact with enterprise systems.

• Develop automated tests for AI applications, APIs, workflows, and integrations.
• Build evaluation datasets and test scenarios for LLM and RAG solutions.
• Evaluate AI outputs for accuracy, relevance, groundedness, completeness, safety, and consistency.
• Perform prompt, model, retrieval, and agent workflow evaluations.
• Troubleshoot AI application issues and perform root-cause analysis.
• Support performance, scalability, resilience, and security testing.

• Deploy AI applications and services into development, test, staging, and production environments.
• Implement CI/CD pipelines for AI applications and supporting services.
• Containerize applications using Docker and deploy to Kubernetes, serverless, or cloud-native environments.
• Implement model, prompt, configuration, and application version management.
• Support LLLOps/MLOps processes including deployment, monitoring, evaluation, and lifecycle management.
• Implement logging, tracing, metrics, token/cost monitoring, and AI observability.