AI Engineer

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.

Experience
8-11 yrs