Roles and Responsibilities:

- Design and develop modern full-stack user experiences for enterprise GenAI applications, copilots, and AI-driven workflows.
- Build responsive front-end interfaces and backend services supporting AI-powered business workflows and operational use cases.
- Develop and integrate conversational AI experiences, approval workflows, agentic interactions, and human-in-the-loop processes.
- Collaborate with Value Engineers and Context Engineers to integrate GenAI services, RAG pipelines, and orchestration frameworks into user-facing applications.
- Build APIs, workflow services, dashboards, and operational interfaces supporting enterprise AI platforms.
- Implement AI evaluation, feedback collection, telemetry, and user interaction monitoring capabilities.
- Design workflows that improve AI usability, explainability, groundedness visibility, and user trust.
- Support validation and testing activities for AI workflows including hallucination checks, workflow alignment, and response quality verification.
- Collaborate with governance and compliance teams to implement responsible AI controls, escalation workflows, and operational guardrails.
- Support user adoption, workflow optimization, and continuous improvement initiatives across GenAI platforms.
- Build reusable UI components and experience patterns for scalable enterprise AI adoption.

Required Qualifications:

- 6+ years of experience in software engineering, platform engineering, AI engineering, or enterprise application development.
- Hands-on experience building Generative AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), prompt engineering, and AI orchestration frameworks.




- Robust programming experience in Python and experience developing APIs, microservices, and enterprise integrations.
- Experience with GenAI frameworks and technologies such as:
- OpenAI/Azure OpenAI
- Semantic Kernel
- LangChain/LangGraph
- Vector databases
- AI orchestration frameworks
- MCP/function-calling architectures
- Experience designing and implementing semantic search, embeddings, knowledge retrieval, and contextual grounding solutions.
- Experience with enterprise GenAI platform implementations in banking, financial services, healthcare, or other regulated industries.
- Familiarity with ModelOps/LLMOps, AI evaluation frameworks (RAGAS, DeepEval, MLflow, etc.), and AI governance practices.
- Experience implementing agentic AI workflows, multi-agent orchestration, and enterprise automation use cases.
- Strong understanding of enterprise application integration patterns and API-driven architectures.
- Experience working with cloud platforms such as Azure, AWS, or GCP and containerized environments such as Docker/Kubernetes.
- Familiarity with CI/CD pipelines, DevSecOps practices, and operational monitoring frameworks.
- Knowledge of AI observability, evaluation methodologies, governance controls, and responsible AI practices.
- Strong analytical, troubleshooting,



and problem-solving skills with the ability to work in fast-paced and evolving technology environments.
- Design and implement enterprise Retrieval-Augmented Generation (RAG) architectures for GenAI platforms and applications.
- Build and optimize semantic retrieval pipelines, vector search implementations, and contextual grounding frameworks.
- Develop ingestion pipelines for enterprise knowledge sources including SharePoint, Confluence, Jira, APIs, databases, and document repositories.
- Define metadata, taxonomy, ontology, chunking, and embedding strategies to improve retrieval relevance and AI response quality.
- Implement permission-aware retrieval and secure knowledge access aligned with enterprise governance and compliance requirements.
- Design and optimize hybrid search architectures combining vector search, keyword search, and knowledge graph capabilities.
- Collaborate with Value Engineers, architects, and business stakeholders to translate enterprise knowledge into scalable AI-ready knowledge structures.
- Improve groundedness, citation accuracy, retrieval precision, and hallucination reduction across GenAI solutions.
- Maintain knowledge lineage, auditability, and contextual traceability for enterprise AI workflows.
- Support AI evaluation, observability, and continuous improvement initiatives for retrieval quality and search performance.
- Work closely with governance, security, and compliance teams to ensure responsible and secure enterprise AI knowledge access.
- Contribute to reusable enterprise knowledge engineering patterns and platform accelerators.

📌 GEN AI Engineer (Hyderabad)
🏢 Orcapod Consulting Services
📍 Hyderabad