Staff Software Engineer - AI

The Hartford•Hartford, CT

About The Position

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future. Position Overview We are seeking a highly motivated, visionary and technically accomplished AI engineer/Software developer to join our team and lead the design, development, deployment, and adoption of AI and machine learning solutions. This role will focus on building agentic AI systems, generative AI workflows, and scalable data platforms that power intelligent decision-making across the enterprise. The ideal candidate will possess deep expertise in AI/ML, software engineering, quality engineering, and cloud-native architectures, with a passion for solving complex problems using cutting-edge technologies.

Requirements

Proficiency in programming languages such as Python, Java, or C++.
UI/End to End Test Automation with Playwright
Performance Load testing
Strong foundation in AI/ML algorithms, data structures, software & quality engineering principles.
Understanding of cloud platforms (e.g., AWS, Azure, GCP) for deploying AI solutions.
Familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, Docker for CI/CD).
Solid knowledge of data structures, algorithms, and software engineering principles.
Experience with agent orchestration, LLMOps, and model lifecycle management, vector search, information retrieval, graph algorithms and knowledge graph.
Experience with version control systems (e.g., Git).
Familiarity with agent orchestration platforms and enterprise integration.
Strong problem-solving skills and ability to work independently and collaboratively.

Nice To Haves

Experience with natural language processing (NLP), computer vision, or reinforcement learning.
Knowledge of generative AI and foundation models (e.g., Gemini, Claude).
Experience with real-time inference systems and edge AI.
Background in mathematics, statistics, or computational neuroscience.
Understanding of LLM (Large Language Model) & LRM (Large Reasoning Model)
Understanding of AI ethics, bias mitigation, and explainable AI.

Responsibilities

Develop Algorithms that enable AI agents to perform tasks without step-by-step instructions.
Design and implement agentic AI systems capable of autonomous decision-making, learning from experience, and adapting to dynamic goals and contexts
Design and develop multi-agent frameworks using tools such as LangGraph, Crew AI, or Semantic Kernel to orchestrate intelligent workflows.
Design and develop machine learning techniques that allow agents to learn from experience and adapt over time.
Translate business requirements into agentic AI solutions.
Collaborate with data scientists, software engineers, business stakeholders, and product teams to integrate AI solutions into production systems.
Conduct context and prompt engineering using zero-shot, few-shot, and chain-of-thought techniques to enhance model performance and relevance.
Optimize agents based on different models for performance, scalability, and accuracy.
Ensure ethical AI practices and compliance with data privacy regulations.
Document processes, models, and code for transparency and reproducibility.
Conduct research and stay up to date with the latest advancements in AI/ML technologies.
Evaluate, implement, and scale AI-powered testing tools (e.g., self-healing automation, test generation, predictive defect analytics).
Leverage machine learning and generative AI to improve: Test case generation, Test data creation, Defect prediction and triaging, Root cause analysis
Drive adoption of intelligent test automation frameworks and autonomous testing strategies.
Partner with Product Managers, Architects, and Engineering leaders to embed quality early in design.
Partner with Product Managers, Architects, Engineering leaders, and RTEs
Communicate status, risks, and improvements to leadership
Influence enterprise adoption of modern Engineering practices across multiple teams and programs.