Generative AI Engineer

Role Overview
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
Data Engineering & Snowflake Integration
•    Build and optimize data pipelines between Snowflake and AI services.
•    Design feature stores and embeddings pipelines using Snowflake.
•    Leverage Snowflake's Cortex LLM functions for in-database AI operations.
•    Implement vector search and semantic search capabilities.
•    Work with structured and unstructured enterprise data.
•    Ensure data quality, governance, and security.
•    Optimize Snowflake queries for AI workloads and cost efficiency.
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).