AI Engineer

Corebridge Financial•Jersey City, NJ

1d•Hybrid

About The Position

We are seeking a highly motivated AI Engineer with 5+ years of experience to design, build, and deploy AI solutions, including modern agentic AI workflows. This role will focus on developing scalable ML models and intelligent agents using foundation models to automate complex business processes and enhance decision-making.

Requirements

Bachelor's or Master's degree in Computer Science, AI, Data Science, or related field
4+ years of experience in AI/ML, data science, or applied AI engineering
Strong programming skills in Python
Solid understanding of machine learning fundamentals and evaluation techniques
Familiarity with prompt engineering and working with large language models (LLMs)
Experience with Retrieval-Augmented Generation (RAG) architectures
Experience working with APIs and integrating AI into applications
Familiarity with orchestration frameworks (e.g., LangChain, Semantic Kernel, or similar)

Nice To Haves

Hands-on experience with Amazon Sagemaker and Bedrock for machine learning and generative AI applications
Familiarity with Bedrock Amazon Guardrails and Knowledge Bases
Hands-on experience with Amazon AgentCore for running agentic AI applications
Experience designing agentic workflows (multi-agent systems, tool-using agents, autonomous workflows)
Experience with cloud platforms such as Amazon Web Services
Knowledge of MLOps practices (Docker, CI/CD, model monitoring)

Responsibilities

Design, develop, and deploy machine learning and AI models for business use cases
Design and fine-tune the underlying models, including reasoning chains, tool use, and agent architectures (ReAct, multi-agent frameworks, etc.)
Define agent capabilities, scope autonomous decision-making boundaries, and manage stakeholder expectations around reliability
Design / optimize prompt instructions, personas, tool descriptions, and reasoning scaffolds that guide agent behavior
Develop and orchestrate multi-step AI agents capable of reasoning, tool usage, and workflow execution
Build agentic workflows leveraging foundation models for task automation and decision support
Test AI agents against enterprise systems, APIs, tools, and data platforms
Design benchmarks and automated evaluations to measure agent reliability, accuracy, and safety across diverse scenarios
Work with structured and unstructured data for predictive and generative AI solutions
Manage and mentor a team of Data Scientists, setting clear expectations for model quality, documentation, and production readiness.
Collaborate with other data scientists, BI teams, and business stakeholders to translate requirements into AI-driven solutions
Optimize model performance, scalability, and cost efficiency
Monitor deployed models/agents and implement feedback loops and retraining strategies
Document technical designs, agent workflows, and system architecture