Software Engineer II – Enterprise AI Products

Jobtailor; Connecticut; On-site; USD 140,000 - 180,000; Full time

Job summary

Jobtailor is seeking a senior AI solutions engineer to design and deliver enterprise AI initiatives across engagements, from strategic discovery to rapid prototypes. You will work with architects, product owners, and LOB leads to translate problems into production-ready architectures using AWS/Azure, Kubernetes, RESTful services, and agentic workflows.

You’ll guide junior engineers and contribute to AI product roadmaps with measurable impact.

Qualifications

• Strong Python experience with additional languages favored (JavaScript/TypeScript/Java).
• Hands-on experience with AWS and/or Azure cloud services.
• Experience with containerization and orchestration tools, including Kubernetes.
• Proficiency with RESTful APIs and microservices architecture.
• Experience with CI/CD tooling such as Jenkins or GitHub Actions.
• Experience designing and delivering agentic workflows, LLM integrations, or AI-powered automations.
• Demonstrated ability to work in agile, collaborative development environments.
• Strong communication skills with non-technical stakeholders.
• Demonstrated experience with AI and automation solutions.
• Experience with Terraform for infrastructure as code.
• Familiarity with MongoDB or NoSQL data stores.
• Familiarity with Temporal or comparable workflow orchestration tools.
• Experience with LLM evaluation methods and metrics.
• Experience with prompt engineering or RAG architectures.
• Exposure to multi-agent frameworks.
• Experience with observability tooling or distributed tracing.
• Familiarity with ML observability and drift detection.
• Familiarity with zero-trust security principles.

Responsibilities

• Design, build, and deliver AI-powered solutions across engagements from strategic initiatives to rapid field prototypes.
• Leverage AWS/Azure, Kubernetes, and containerization to implement enterprise AI solutions with high accuracy and speed.
• Collaborate with architects and product leads to translate business problems into producible tech designs.
• Own end-to-end delivery from scoping to knowledge transfer while upholding code quality and design standards.
• Engage with LOB stakeholders to surface adjacent needs and inform AI product roadmaps.
• Provide technical guidance and coaching to junior engineers as an informal tech lead.
• Demonstrate subject matter expertise across multiple technology disciplines for LOB partners and internal teams.

Skills

Python

JavaScript/TypeScript/Java

AWS

Azure

Kubernetes

Containerization

REST APIs

Microservices

CI/CD

Jenkins

GitHub Actions

LLM integrations

Agentic workflows

Agile

Communication

AI/Automation

Terraform

MongoDB

NoSQL

Temporal

LLM evaluation

Prompt engineering

RAG architectures

Multi-agent frameworks

Observability

Distributed tracing

ML observability

Security best practices

Job description

Responsibilities

• Design, build, and deliver AI-powered solutions across a range of engagement types — from sustained strategic initiatives requiring deep discovery and architecture work, to rapid field engagements focused on integrating LOB workflows with enterprise AI platforms and validating feasibility through working prototypes.
• Leverage enterprise AI capabilities including large language models, agentic workflows, cloud computing services (AWS/Azure), and container orchestration (Kubernetes) to solve complex, real-world business problems with a high degree of accuracy and speed.
• Contribute to application and solution design in partnership with architects, product owners, and LOB technical leads — translating ambiguous business problems into technically sound, implementable solutions with clear production intent from kickoff.
• Own delivery end to end — from scoping and design through development, handoff, and knowledge transfer — maintaining high standards for code quality and solution design at every stage.
• Actively engage with LOB stakeholders to understand not just the problem at hand, but adjacent challenges and emerging needs — surfacing field intelligence that informs the broader AI product roadmap and strategic investment decisions, and sharing those findings in a discoverable way across the value stream.
• Provide technical guidance and coaching to junior engineers on the team, serving as an informal technical lead within your team — setting the bar for code quality, solution design, and delivery standards without carrying formal management responsibility.
• Demonstrate subject matter expertise across multiple technology disciplines, serving as a credible technical resource for LOB partners and internal engineering teams.

Requirements

• Proficiency in Python, with experience in additional languages such as JavaScript, TypeScript, or Java welcomed — particularly in candidates with backgrounds in enterprise AI integration.
• Hands-on experience with AWS and/or Azure cloud services.
• Experience with containerization and orchestration tools, including Kubernetes.
• Proficiency with RESTful APIs and microservices architecture.
• Experience with CI/CD tooling such as Jenkins or GitHub Actions.
• Experience designing and delivering agentic workflows, LLM integrations, or AI-powered automations.
• Demonstrated ability to work in agile, collaborative development environments.
• Strong communication skills — able to engage directly with non-technical business stakeholders and translate operational problems into technical solutions.
• Demonstrated experience with AI and automation solutions.
• Experience with Terraform for infrastructure as code.
• Familiarity with MongoDB or comparable NoSQL data stores.
• Familiarity with Temporal or comparable workflow orchestration tools.
• Experience with LLM evaluation methods, including designing evaluation datasets, defining metrics, and measuring model performance in production.
• Experience with prompt engineering or RAG architectures.
• Exposure to MCP server configurations or multi-agent frameworks.
• Experience with observability tooling or distributed tracing in production environments.
• Familiarity with ML observability and drift detection in production environments.
• Familiarity with zero-trust security principles or secure-by-design development practices.