Tech Consultant - Agentic AI Engineer

Role: Tech Consultant - Agentic AI Engineer

Location: NYC/NJ, Tempe, AZ/ Tampa, FL (Hybrid)

Type: Contract

Role Summary:

• We are seeking a hands-on Agentic AI Engineer with strong expertise in designing, developing, and deploying production-grade AI agents, LLM-powered applications, RAG solutions, and AI-enabled workflows.
• Agentic AI and AI Engineering capabilities are the primary criteria for this role. The ideal candidate should have at least 2+ years of hands-on AI Engineering experience, with demonstrated experience taking AI solutions beyond prototypes into production.
• The candidate should also possess a solid Data Engineering foundation, including data ingestion, transformation, management, and delivery. Data Engineering will complement the primary AI Engineering responsibilities and enable the development of reliable, enterprise-grade AI solutions.

Day to Day Job Duties

• Design, develop, and deploy production-grade Agentic AI and LLM-based solutions.
• Build AI agents capable of reasoning, retrieval, tool invocation, and multi-step workflow execution.
• Design and implement RAG (Retrieval-Augmented Generation) solutions using enterprise data and knowledge sources.
• Develop prompt strategies, reusable prompt templates, and context-management approaches for LLM applications.
• Integrate LLMs and AI agents with enterprise applications, APIs, databases, and external tools.
• Develop backend AI services and APIs using Python.
• Implement agent/tool execution workflows with appropriate authentication, authorization, and security controls.
• Develop retrieval pipelines incorporating document processing, embeddings, vector search, and contextual retrieval.
• Implement evaluation frameworks to measure retrieval quality, response quality, agent behavior, and overall solution performance.
• Deploy AI applications and agents to cloud environments and support production operations.
• Implement monitoring, logging, tracing, alerting, and observability for AI/LLM applications.
• Troubleshoot production AI systems and continuously improve accuracy, reliability, latency, and operational performance.
• Develop and maintain data ingestion and transformation pipelines required by AI applications.
• Collaborate with Data Engineers, Data Scientists, Software Engineers, Architects, Security teams, and business stakeholders.
• Rapidly develop POCs/MVPs while ensuring successful solutions can evolve into secure and scalable production implementations.

Basic Qualifications Must Have:

• 2+ years of hands-on experience in AI Engineering, including development of LLM, Generative AI, or Agentic AI solutions.
• 2+ years of Strong hands-on experience with Agentic AI, LLMs, Prompt Engineering, and Retrieval-Augmented Generation (RAG).
• 2+ years of Proven production development experience using Python, APIs, cloud platforms, and enterprise security practices.
• Demonstrated experience delivering at least one complete production AI solution covering retrieval, tool execution, evaluation, deployment, and monitoring.
• Solid experience with Data Engineering, data ingestion, data transformation, data management, and data delivery.

Core AI Engineering Skills:

• Agentic AI: AI Agents, Agent Orchestration, Tool/Function Calling, Multi-Step Workflows
• Generative AI: LLMs, Prompt Engineering, Context Management
• RAG: Retrieval, Embeddings, Vector Search, Knowledge Retrieval
• Development: Python, REST APIs, Microservices
• Production AI: Deployment, Evaluation, Monitoring, Observability
• Security: Authentication, Authorization, Data Security, Guardrails
• Cloud: AWS, Azure, or Google Cloud Platform
• Data: Data Ingestion, Transformation, Processing & Delivery

Preferred / Strong Differentiators:

• Experience with agent frameworks such as LangGraph, LangChain, CrewAI, AutoGen, Strands, or similar technologies.
• Experience with AWS Bedrock, Azure AI Foundry/OpenAI, Anthropic, OpenAI, or equivalent enterprise AI platforms.
• Experience developing tool-calling agents and integrations with enterprise systems.
• Knowledge of Model Context Protocol (MCP).
• Experience with vector databases and enterprise search platforms.
• Experience implementing LLM/agent evaluation, tracing, and observability.
• Knowledge of AI guardrails, prompt-injection protection, audit logging, and Responsible AI practices.
• Experience building production-grade data pipelines supporting AI applications.
• Strong background in Data Engineering, data platforms, data management, ingestion, and delivery.
• Experience with Databricks, Spark/PySpark, Snowflake, or similar modern data platforms.
• Experience with BI, analytics, reporting, and enterprise data solutions.
• Experience within Banking or Financial Services is preferred.
• Strong software engineering, CI/CD, cloud-native development, and production support experience.