AI Engineer

About the Role:

• We are seeking an AI Engineer to design and develop advanced agentic systems, AI-driven accelerators, and enterprise-grade applications powered by LLMs and multimodal models. This is a hands-on DevOps / Platform Engineering role with significant influence over architectural decisions, experimentation strategy, and technical direction. You'll work closely with product leads, data scientists, and platform engineers to deliver scalable, secure, and context-aware AI Ops solutions that drive automation, intelligence, and productivity across domains.

Key Responsibilities:

• Design and develop autonomous AI agents and multi-step workflows that utilize LLMs, tool use, and memory/context chaining.
• Build custom accelerators to automate knowledge tasks (e.g., analytics generation, support automation, code generation).
• Fine-tune or adapt foundation models (e.g., OpenAI, Claude, Mistral, LLaMA) for targeted use cases.
• Integrate agentic capabilities with APIs, vector stores, databases, and third-party tools using orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, etc.).
• Define architecture, data flows, and security protocols for scalable GenAIapplications.
• Partner with stakeholders to identify high-value AI opportunities and quickly prototype viable solutions.
• Stay current on LLM research and industry trends; advise on model and framework selection.

Required Skills and Experience:

• 2+ years in DevOps experience with familiarity to AI/ML systems; AI or LLM-based application development / DevOps experience preferred
• Strong experience with Python (preferred), TypeScript, or similar languages.
• Proven experience building agentic systems using LangChain, AutoGen, Semantic Kernel, or custom orchestration.
• Experience with prompt engineering, RAG (retrieval-augmented generation), and vector databases (e.g., Pinecone, FAISS, Weaviate).
• Deep understanding of LLM APIs and models (e.g., OpenAI GPT-4, Claude, LLaMA, Mistral).
• Familiarity with cloud-native development (AWS), containers, and CI/CD pipelines.
• Strong product thinking, with the ability to translate user needs into impactful AI solutions.

Preferred Qualifications:

• Experience with multi-agent systems, tool-use chaining, and long-term memory persistence.
• Exposure to MLOps practices and model deployment pipelines.
• Knowledge of privacy, security, and compliance in AI applications.