About the role
We are looking for an experienced AI Engineer to build and deploy production-grade Generative AI and Agentic AI solutions. The ideal candidate will have strong software engineering fundamentals combined with hands-on experience in LLMs, RAG pipelines, agentic workflows, API integrations, and AI application development. This role involves designing, developing, and optimizing AI-powered applications while ensuring scalability, security, observability, and cost efficiency.
Responsibilities:
- Build and deploy Generative AI and Agentic AI solutions in production environments.
- Develop and maintain RAG (Retrieval-Augmented Generation) pipelines, embeddings, vector search, and document ingestion workflows.
- Design and implement agentic workflows using frameworks such as LangChain, CrewAI, AutoGen, Semantic Kernel, or similar.
- Integrate AI systems with enterprise applications, APIs, MCP servers, databases, and external services.
- Implement prompt engineering, structured outputs, tool/function calling, and model orchestration.
- Create evaluation frameworks, test suites, and monitoring mechanisms to improve solution quality and reliability.
- Implement guardrails, security controls, and responsible AI practices.
- Optimize model usage through caching, batching, prompt optimization, and cost-aware design.
- Collaborate with product, engineering, and business stakeholders to deliver scalable AI solutions.
Requirements:
- Strong problem-solving and software engineering mindset.
- Ability to build production-ready AI solutions, not just prototypes.
- Strong ownership, collaboration, and communication skills.
- Passion for Generative AI, Agentic Systems, and emerging AI technologies.
- 8-11 years of software engineering experience.
- Strong hands-on experience with Generative AI, LLMs, Agentic AI, and AI application development.
- Experience with OpenAI, Azure OpenAI, Anthropic, Gemini, or similar LLM platforms.
- Strong expertise in RAG, Vector Databases, Embeddings, Semantic Search, and Knowledge Retrieval Systems.
- Experience with LangChain, CrewAI, AutoGen, Semantic Kernel, or equivalent orchestration frameworks.
- Strong backend development experience with APIs, microservices, and integrations.
- Experience working with cloud platforms such as AWS, Azure, or GCP.
- Familiarity with Docker, Kubernetes, and modern deployment practices.
- Understanding of observability, monitoring, logging, and production support.
- Knowledge of AI security, privacy, governance, and safe AI implementation practices.
Preferred Skills:
- Experience building enterprise-scale AI platforms and workflows.
- Exposure to workflow automation, enterprise integrations, and event-driven architectures.
- Experience working with sensitive or regulated data environments.
- Contributions to reusable AI frameworks, internal tooling, or developer platforms.