AI Engineer

We are seeking an AI Engineer to design, develop, and deploy scalable LLM-powered solutions leveraging AWS cloud services, Snowflake, and modern GenAI frameworks. This role focuses on building production-grade AI systems, optimizing LLM inference, and integrating enterprise data platforms with cutting-edge AI technologies.

The ideal candidate combines strong cloud engineering expertise with hands-on experience in prompt engineering, foundation models, agentic AI systems, and data pipelines within Snowflake and AWS ecosystems

Key Responsibilities

AI & Generative AI Development

• Design, develop, and deploy LLM-powered applications and agentic AI systems in production environments.
• Implement advanced prompt engineering strategies including:
• Prompt chaining and multi-turn orchestration
• Few-shot learning and in-context learning
• Chain-of-Thought (CoT) and Tree-of-Thought (ToT) prompting
• Function calling and tool use optimization
• Structured output generation (JSON, XML schemas)
• Build and optimize Retrieval-Augmented Generation (RAG) systems integrating Snowflake data with LLMs.
• Evaluate and fine-tune foundation models via AWS Bedrock or other managed AI services.
• Develop guardrails for AI systems including hallucination mitigation, grounding, and safety controls.
• Implement LLMOps best practices for model lifecycle management:
• Model versioning, deployment, and rollback strategies
• Prompt versioning and experimentation frameworks
• Monitor and observe LLM application performance using observability tools.
• Evaluation frameworks for LLM outputs

Cloud & Platform Engineering (AWS)

Architect scalable AI solutions using AWS services such as:

• Bedrock - Sagemaker – Access and fine-tune foundation models
• Lambda – Serverless LLM application deployment
• EC2 – GPU-accelerated inference and batch processing
• Step Functions – Orchestrate complex LLM workflows and agentic pipelines
• CloudWatch – Monitoring, logging, and alerting for AI systems

AI Application Development

• Build APIs and backend services to operationalize AI solutions.
• Integrate LLM/AI systems into internal applications, sales tools, or analytics platforms.
• Implement streaming and real-time inference for low-latency AI applications.
• Collaborate with stakeholders to translate use cases into production AI systems.

Required Qualifications

• 5+ years of experience in AI/ML, Software or Data engineering.
• Proficiency in Python with solid understanding of ML fundamentals
• Strong hands-on experience with AWS, APIs and microservices architecture
• Experience integrating AI solutions with data systems like Snowflake.
• Practical experience with prompt engineering
• Experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel, or similar
• Experience with agentic frameworks (AutoGen, CrewAI, or equivalent).

Preferred Qualifications

• Experience building RAG pipelines in enterprise environments.
• Knowledge of MLOps best practices.
• Experience with vector databases and embeddings.
• Familiarity with model evaluation frameworks (e.g., LLM eval metrics).
• Experience implementing AI governance and responsible AI practices.
• Background in sales, media, marketing analytics, or enterprise data platforms (a plus). #LI-MS1