Description

What You'll Do:

• Design and develop agentic AI systems including single-agent and multi-agent workflows
• Implement planning, memory, tool-use patterns, and human-in-the-loop processes
• Build, deploy, and scale production-grade GenAI solutions
• Integrate AI agents with enterprise applications, APIs, and data sources using secure and governed access mechanisms
• Implement Retrieval-Augmented Generation (RAG), enterprise knowledge grounding, and vector search solutions
• Contribute to LLMOps / AgentOps activities such as evaluation, monitoring, debugging, and optimization
• Ensure Responsible AI practices including security, access control, governance, auditability, and compliance
• Support observability, performance tuning, and cost optimization of AI workloads
• Collaborate with cross-functional teams and mentor junior engineers as required
   

Expertise You'll Bring:

• 3-9 years of experience in software engineering, AI engineering, or solution development roles
• At least 1 year of hands-on experience building GenAI or LLM-based solutions
• Strong hands-on Python development skills
• Experience with agentic design patterns, tool-use, and basic multi-agent orchestration
• Hands-on experience with RAG pipelines, vector databases, and enterprise knowledge systems
• Exposure to LLMOps / AgentOps practices such as monitoring and optimization
• Experience working with at least one major AI platform: Azure (Azure OpenAI / AI Foundry), AWS (Amazon Bedrock), or GCP (Vertex AI)
• Familiarity with open-source and proprietary LLMs, including cost, performance, and security considerations
• Experience delivering production or near-production AI solutions
• Educational background: Bachelor's or master's degree in engineering, Computer Science, or related field

Key Skills

Education

Any Graduate