AI Engineer
Job Description: Agentic AI Engineer (2+Years Experience)
Role Overview
We are seeking an experienced Agentic AI Engineer to design, build, and evolve scalable agent-based AI applications and platforms. This role requires strong hands-on expertise in distributed system design, and modern agentic AI frameworks to deliver autonomous, production-grade AI systems.
You will work closely with product, data, and engineering teams to architect and implement intelligent agent workflows, LLM pipelines, and memory-driven reasoning systems in a cloud-agnostic environment.
Key Responsibilities
● Implement agent-based systems, including orchestration, planning, execution, and memory management
● Build and deploy LLM-driven pipelines, including prompt strategies, tool invocation, and retrieval-augmented generation (RAG)
● Design vector-based memory systems and hybrid retrieval mechanisms (vector + keyword + structured data)
● Drive technical decisions related to architecture, tooling, and infrastructure, ensuring performance, reliability, and extensibility
● Partner with Data Scientist to develop and optimize ML models, pipelines, and orchestration logic for real-world use cases
● Collaborate within Agile teams to deliver high-quality, production-ready solutions
Required Experience & Expertise
Professional Experience
● 2 years of industry experience in AI/ML and Intelligent systems development
● Proven experience delivering AI or ML solutions in large-scale or enterprise environments
Agentic AI & Systems
● Strong understanding of Agentic AI architectures, including both neural-based and symbolic agents
● Hands-on experience building multi-agent systems, including:
○ Agent collaboration and coordination
○ Reinforcement learning or feedback-driven optimization
○ Dynamic or flexible workflows
○ State, caching, and memory management
● Experience with one or more agentic AI frameworks, such as:
○ LangGraph / LangChain
○ CrewAI
○ Semantic Kernel
○ AutoGen or equivalent frameworks
Programming & ML
● Strong proficiency in Python for building scalable, production-grade systems
● Experienced or foundational knowledge in machine learning frameworks such as
TensorFlow, PyTorch, Scikit-learn, or AutoML tools
● Solid understanding of model lifecycle management, including training, evaluation, and deployment
Prompt Engineering & LLMs
● Practical experience with prompt engineering techniques, including:
○ Zero-shot and few-shot prompting
○ Chain-of-thought and structured reasoning
○ Prompt iteration and optimization
● Experience building LLM-based applications, including tool use and function calling
IR / RAG & Knowledge Systems
● Experience designing and implementing Information Retrieval (IR) and RAG systems
● Hands-on work with vector databases, embeddings, and optionally knowledge graphs
● Familiarity with hybrid search approaches (vector + lexical + metadata-based retrieval)
Model Evaluation
● Experience evaluating AI systems using quantitative and qualitative metrics
● Familiarity with A/B testing, benchmarking, and performance analysis of LLMs and prompts
Technical Skills
● Programming Languages: Python (required)
● Agentic AI: LangGraph, LangChain, CrewAI, Semantic Kernel, AutoGen, OpenAI Agent SDK, or similar
● Generative AI: LLMs, RAG architectures, NLP pipelines
● Cloud Platforms: Experience with at least one major cloud provider (e.g., GCP, Azure,
AWS); ability to design cloud-agnostic architectures
● Version Control: Git / GitHub
● Development Practices: Model testing, validation, CI/CD awareness
● Collaboration: Experience working in Agile / Scrum teams
What Success Looks Like
● The candidate independently designs and implements enterprise-grade agentic AI solutions with minimal supervision and zero hand-holding.
● Translates ambiguous or loosely defined business requirements into well-architected, scalable, and production-ready agentic systems.
● Delivers solutions that adhere to enterprise IT, security, and compliance standards, including data governance and access controls.
● Builds fault-tolerant, resilient agentic software, with clear handling of both success and failure scenarios.
● Implements comprehensive testing strategies, covering positive paths, edge cases, and failure modes, incorporating explicit business validation inputs.
● Proactively collaborates with cross-functional team members to promote shared learning, technical excellence, and best practices.
● Acts as a reliable team contributor during high-pressure situations, including production incidents or critical system failures, supporting root-cause analysis and rapid recovery.
● Demonstrates ownership, accountability, and a production-first mindset throughout the lifecycle of agentic AI solutions.
Role Overview
We are seeking an experienced Agentic AI Engineer to design, build, and evolve scalable agent-based AI applications and platforms. This role requires strong hands-on expertise in distributed system design, and modern agentic AI frameworks to deliver autonomous, production-grade AI systems.
You will work closely with product, data, and engineering teams to architect and implement intelligent agent workflows, LLM pipelines, and memory-driven reasoning systems in a cloud-agnostic environment.
Key Responsibilities
● Implement agent-based systems, including orchestration, planning, execution, and memory management
● Build and deploy LLM-driven pipelines, including prompt strategies, tool invocation, and retrieval-augmented generation (RAG)
● Design vector-based memory systems and hybrid retrieval mechanisms (vector + keyword + structured data)
● Drive technical decisions related to architecture, tooling, and infrastructure, ensuring performance, reliability, and extensibility
● Partner with Data Scientist to develop and optimize ML models, pipelines, and orchestration logic for real-world use cases
● Collaborate within Agile teams to deliver high-quality, production-ready solutions
Required Experience & Expertise
Professional Experience
● 2 years of industry experience in AI/ML and Intelligent systems development
● Proven experience delivering AI or ML solutions in large-scale or enterprise environments
Agentic AI & Systems
● Strong understanding of Agentic AI architectures, including both neural-based and symbolic agents
● Hands-on experience building multi-agent systems, including:
○ Agent collaboration and coordination
○ Reinforcement learning or feedback-driven optimization
○ Dynamic or flexible workflows
○ State, caching, and memory management
● Experience with one or more agentic AI frameworks, such as:
○ LangGraph / LangChain
○ CrewAI
○ Semantic Kernel
○ AutoGen or equivalent frameworks
Programming & ML
● Strong proficiency in Python for building scalable, production-grade systems
● Experienced or foundational knowledge in machine learning frameworks such as
TensorFlow, PyTorch, Scikit-learn, or AutoML tools
● Solid understanding of model lifecycle management, including training, evaluation, and deployment
Prompt Engineering & LLMs
● Practical experience with prompt engineering techniques, including:
○ Zero-shot and few-shot prompting
○ Chain-of-thought and structured reasoning
○ Prompt iteration and optimization
● Experience building LLM-based applications, including tool use and function calling
IR / RAG & Knowledge Systems
● Experience designing and implementing Information Retrieval (IR) and RAG systems
● Hands-on work with vector databases, embeddings, and optionally knowledge graphs
● Familiarity with hybrid search approaches (vector + lexical + metadata-based retrieval)
Model Evaluation
● Experience evaluating AI systems using quantitative and qualitative metrics
● Familiarity with A/B testing, benchmarking, and performance analysis of LLMs and prompts
Technical Skills
● Programming Languages: Python (required)
● Agentic AI: LangGraph, LangChain, CrewAI, Semantic Kernel, AutoGen, OpenAI Agent SDK, or similar
● Generative AI: LLMs, RAG architectures, NLP pipelines
● Cloud Platforms: Experience with at least one major cloud provider (e.g., GCP, Azure,
AWS); ability to design cloud-agnostic architectures
● Version Control: Git / GitHub
● Development Practices: Model testing, validation, CI/CD awareness
● Collaboration: Experience working in Agile / Scrum teams
What Success Looks Like
● The candidate independently designs and implements enterprise-grade agentic AI solutions with minimal supervision and zero hand-holding.
● Translates ambiguous or loosely defined business requirements into well-architected, scalable, and production-ready agentic systems.
● Delivers solutions that adhere to enterprise IT, security, and compliance standards, including data governance and access controls.
● Builds fault-tolerant, resilient agentic software, with clear handling of both success and failure scenarios.
● Implements comprehensive testing strategies, covering positive paths, edge cases, and failure modes, incorporating explicit business validation inputs.
● Proactively collaborates with cross-functional team members to promote shared learning, technical excellence, and best practices.
● Acts as a reliable team contributor during high-pressure situations, including production incidents or critical system failures, supporting root-cause analysis and rapid recovery.
● Demonstrates ownership, accountability, and a production-first mindset throughout the lifecycle of agentic AI solutions.
Job Type
Full-Time Regular
Full-Time Regular
Location
Hybrid
Hybrid
Location
Tampa FL
Tampa FL