Job Summary

We are seeking a Principal AI Solution Engineer / AI Architect with a minimum of 8 years of overall technology experience and a strong record of designing and delivering multiple production-grade AI solutions. This is a hands-on technical leadership role responsible for shaping enterprise AI architecture, translating business needs into scalable solutions, and guiding engineering teams from experimentation through secure production deployment.

The ideal candidate combines deep expertise in Azure AI Foundry, Azure OpenAI, Azure AI Search, retrieval-augmented generation (RAG), and agent-based systems with strong enterprise integration skills across REST APIs, Oracle Database, Oracle APEX, and the Microsoft ecosystem. The role requires sound architectural judgement, practical engineering depth, stakeholder influence, and the ability to establish reusable patterns for responsible AI adoption.

Responsibilities

- Own the end-to-end architecture and technical delivery of enterprise AI solutions, from discovery and proof of concept through production rollout and continuous improvement.
- Partner with business, product, data, security, infrastructure, and engineering stakeholders to identify high-value use cases, define measurable outcomes, and select the appropriate AI solution pattern.
- Design secure, scalable, reliable, and cost-conscious solutions using Azure AI Foundry, Azure OpenAI, Azure AI Search, and appropriate agent frameworks.
- Architect RAG solutions, including document ingestion, chunking, embeddings, vector indexing, hybrid retrieval, reranking, grounding, citation, and response-generation strategies.
- Design AI agents and agentic workflows using tool calling, orchestration, state and memory patterns, guardrails, exception handling, and human-in-the-loop approvals.
- Integrate AI capabilities with enterprise applications and data sources through REST APIs, Oracle Database, Oracle APEX, and Microsoft ecosystem services.
- Establish reference architectures, reusable components, engineering standards, prompt patterns, evaluation methods, and deployment practices for AI solutions.
- Define and implement controls for responsible AI, data privacy, security, access management, content safety, auditability, and regulatory or organizational compliance.
- Create evaluation frameworks for answer quality, retrieval relevance, grounded ness, safety, latency, reliability, user adoption, and cost; use findings to improve solutions iteratively.
- Provide hands-on technical guidance, conduct architecture and code reviews, troubleshoot complex issues, and help teams make pragmatic build-versus-buy decisions.
- Mentor senior engineers and AI champions, accelerate capability development across application teams,



and promote disciplined experimentation and knowledge sharing.
- Communicate architecture decisions, trade-offs, delivery risks, and business value clearly to both technical teams and senior leadership.
- Contribute to Agile planning, estimation, dependency management, release readiness, production support, and post-implementation reviews.

Required Skills

AI Platforms and Solution Architecture

- Strong hands-on experience with Azure AI Foundry and Azure OpenAI for building, evaluating, deploying, and operating generative AI solutions.
- Experience with Azure AI Search, including vector search, hybrid search, semantic ranking, indexing strategies, filters, and security-aware retrieval.
- Practical experience with agent frameworks and orchestration patterns for enterprise use cases.
- Ability to design cloud-based AI architectures that balance scalability, availability, security, performance, maintainability, and cost.
- Understanding of model selection, model limitations, context management, token usage, latency, throughput, rate limits, and fallback strategies.

Retrieval-Augmented Generation (RAG)

- Deep understanding of embeddings, vector databases, retrieval architectures, and grounding strategies.
- Experience designing ingestion and retrieval pipelines for structured and unstructured enterprise content.
- Knowledge of chunking, metadata design, query transformation, hybrid retrieval, reranking, citation, access controls, and freshness strategies.
- Ability to evaluate and improve retrieval relevance, answer groundedness, completeness, and hallucination risk.

Agent Development

- Understanding of single-agent and multi-agent workflows, agent orchestration, planning, routing, state, memory, and task decomposition.
- Experience with function or tool calling, structured outputs, API-based actions, error recovery, and observability.
- Ability to design human-in-the-loop approval and escalation controls for sensitive or high-impact actions.
- Knowledge of guardrails that prevent unauthorized, unsafe, or unintended agent behavior.

Prompt Engineering and AI Evaluation

- Advanced prompt engineering skills, including system instructions, few-shot prompting, decomposition, structured output, grounding, and reusable prompt templates.
- Experience designing automated and human evaluation approaches,



test datasets, quality rubrics, regression tests, and production feedback loops.
- Understanding of prompt injection, data leakage, hallucination, bias, content safety, and mitigation techniques.

Enterprise Integration

- Strong experience designing and integrating REST APIs and JSON-based services, including authentication, authorization, throttling, resilience, and error handling.
- Working knowledge of Oracle Database, SQL, data access patterns, and secure integration with enterprise data.
- Experience integrating AI capabilities into Oracle APEX or comparable enterprise and low-code application platforms.
- Good understanding of the Microsoft ecosystem and how AI solutions integrate with Azure services, identity, security, monitoring, and collaboration platforms.
- Experience with event-driven, batch, and real-time integration patterns is preferred.

Data and Analytical Understanding

- Sufficient knowledge of time-series analysis, forecasting, and regression to assess use cases, collaborate with data specialists, interpret model outputs, and determine when classical analytical approaches are more appropriate than generative AI.
- Understanding of data quality, feature selection, training and validation concepts, model performance measures, and basic statistical reasoning.
- Ability to distinguish between generative AI, traditional machine learning, deterministic rules, and hybrid solution approaches.

Engineering, DevOps, and Operations

- Solid software engineering fundamentals and proficiency in at least one enterprise programming language, preferably Python, JavaScript or TypeScript.
- Experience with Git, automated testing, CI/CD, infrastructure and environment configuration, secrets management, and production release controls.
- Experience implementing logging, tracing, monitoring, usage analytics, quality telemetry, and cost monitoring for AI workloads.
- Understanding of API security, identity and access management, encryption, privacy, and secure software development practices.

Leadership and Collaboration

- Operate as the senior technical authority for assigned AI initiatives while remaining hands-on during critical design and delivery phases.
- Influence architecture and engineering decisions across teams without relying solely on formal authority.
- Mentor engineers, review designs and code, and establish practical standards that improve delivery quality and reuse.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Principal AI Solution Engineer / AI Architect (Hyderabad)
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