Company Description
Omnie Solutions (I) Pvt Ltd is a global enterprise solutions provider focused on delivering sustainable business outcomes and increased operational efficiency for clients. With a growing team of around 300 professionals worldwide, the company emphasizes long-term client partnerships and collaborative solution-building. Omnie specializes in Enterprise Application Integration and customer-facing applications, including enterprise mobile apps and e-commerce platforms. The organization works with flexible onsite and offshore models, leveraging Microsoft, IBM, and Oracle-Siebel technologies, and holds Gold Partnerships that validate the quality and reliability of its solutions. Its technology-driven approach has been recognized through multiple case studies, including a Microsoft-published SharePoint 2010 global implementation.
Job Description
We are looking for a highly skilled AI Agent Engineer / Generative AI Engineer with hands-on experience in building and deploying enterprise-grade Generative AI and AI Agent solutions using AWS Bedrock as the primary platform and Google Gemini / Vertex AI Agent Builder as a secondary ecosystem.
The ideal candidate should have strong expertise in AI workflow orchestration, prompt engineering, tool calling, guardrails, conversation memory management, and evaluation frameworks for LLM-powered applications.
This role requires a blend of backend engineering, GenAI architecture, and AI product thinking to build scalable, secure, and observable AI systems for enterprise use cases.
Key Responsibilities
AI Agent Development
- Design, develop, and deploy AI agents using Amazon Web Services Bedrock.
- Build multi-step agentic workflows with tool calling, orchestration logic, and contextual reasoning.
- Integrate external APIs, enterprise systems, databases, and knowledge sources into AI workflows.
Prompt Engineering & Orchestration
- Develop advanced prompt orchestration frameworks for reliable and scalable AI interactions.
- Optimize prompts for accuracy, latency, safety, and cost efficiency.
- Implement prompt chaining, contextual retrieval, and response grounding techniques.
Tool Calling & Workflow Design
- Build structured tool-calling frameworks for enterprise AI applications.
- Design agent workflows involving function calling, task decomposition, and autonomous execution patterns.
- Develop reusable orchestration pipelines for AI-driven automation.
Guardrails & AI Safety
- Design and implement AI safety policies, moderation layers, and governance mechanisms.
- Create guardrails for hallucination control, prompt injection prevention, and secure AI responses.
- Ensure responsible AI practices and enterprise compliance standards.
Conversation Memory & Session Management
- Implement short-term and long-term conversation memory architectures.
- Design session handling and context persistence for conversational AI systems.
- Optimize memory retrieval and contextual continuity for multi-turn interactions.
AI Evaluation & Observability
- Build evaluation pipelines for LLM performance testing and benchmarking.
- Monitor AI application performance including latency, token usage, hallucination rates, and response quality.
- Implement observability frameworks, logging, tracing, and feedback loops for AI systems.
Cloud & Deployment
- Deploy scalable AI services on AWS infrastructure.
- Work closely with DevOps and backend teams for production-grade deployment and monitoring.
- Contribute to architecture discussions and platform optimization.
Required Skills & Qualifications
Mandatory Skills
- 3–6 years of overall software engineering or AI application development experience.
- Minimum 1+ years of hands-on experience with Amazon Web Services Bedrock/Vertex AI.
- Strong understanding of Generative AI, LLMs, and AI Agent architectures.
- Experience in:
- Prompt orchestration
- Tool calling and workflow design
- AI guardrails and policy frameworks
- Conversation memory/session handling
- AI evaluation and observability
- Strong experience with Python and API integrations.
- Understanding of vector databases, embeddings, and RAG architectures.
- Familiarity with REST APIs, microservices, and cloud-native systems.
Education
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.
