Job Description :

We are seeking a motivated Senior AI Engineer to design, implement, and deploy cutting-edge AI solutions powered by large language models (LLMs).

Responsibilities & Deliverables :

- Develop key AI architecture patterns (RAG, text-to-sql, multi-agent, etc).

- Create end-to-end production grade AI agents and integrate them into applications.

- Apply prompt engineering and context engineering techniques to optimize LLM performance.

- Implement function calling and tool use patterns for agentic workflows.

- Build and maintain AI workflows and pipelines using LangChain/LangGraph or equivalent.

- Deploy and manage LLM solutions on Azure OpenAI, AWS Bedrock, or similar cloud platforms.

- Implement observability and monitoring for AI systems to track performance, costs, and reliability.

- Automate model deployment and orchestration using CI/CD pipelines.

- Evaluate, benchmark, and document models.

- Design and implement Model Context Protocol (MCP) integrations and tool orchestration frameworks for scalable agentic systems.

- Build secure tool invocation and multi-system orchestration pipelines across enterprise services.

- Implement security controls for AI systems, including data access governance, PII handling, and safe tool usage.

- Ensure secure and compliant AI deployments aligned with enterprise and regulatory standards.

Required Skills & Experience :

- Programming : Strong proficiency in Python for AI/ML development.

- LLM Fundamentals : Understanding of large language models, their inner workings, capabilities, and limitations.

- AI Engineering Techniques : Knowledge of prompt engineering, context engineering, AI agent patterns (RAG, text-to-sql, etc).

- AI Frameworks : Hands-on experience with at least one framework : LangChain, LangGraph, CrewAI, or similar.

- Vector Databases : Familiarity with vector stores (Pinecone, Weaviate, ChromaDB, FAISS, etc.) for embeddings and retrieval.

- Software Development : Understanding of Git workflows, version control with GitHub, and software development best practices.

- Collaboration : Experience working in Agile/Scrum teams with sprint-based development.

- Strong problem-solving skills and ability to learn new technologies quickly.

- Exposure to AI-assisted development tooling (e.g. GitHub CoPilot, Claude Code, Cursor, or equivalent).

- Experience with MCP (Model Context Protocol) or similar tool integration/orchestration frameworks.

- Strong understanding of AI system security, including :

1. Prompt injection risks.

2. Data leakage prevention.

3. Access control (RBAC/ABAC) for tools and data.

- Experience building secure, enterprise-grade AI solutions with governance and compliance consideration.

Nice to have :

- Experience with enterprise AI platforms, multi-agent systems, or internal tool ecosystems.

- Familiarity with auditability, observability, and evaluation frameworks for LLMs.