Applied AI Engineer

DXC Luxoft; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Luxoft is seeking an applied AI engineer for an energy solutions platform. You will build AI features across data onboarding, retrieval and agent orchestration, partnering with product teams to define success metrics and scale AI capabilities.

The role emphasizes production-grade LLMs, evaluation, monitoring, and collaboration with data engineers and software teams to deploy reliable AI-driven solutions for energy portfolios.

Qualifications

• Shipped LLM-based features or agents to production.
• Experience with Python or Node.js/TypeScript in production.
• Proficient in prompt design, tool integration and structured outputs.
• Experience with retrieval/RAG tooling and agent orchestration.
• Familiar with evaluation frameworks, monitoring, and production quality metrics.
• Cloud experience (AWS preferred) and cloud AI services.

Responsibilities

• Define high-value AI use cases with product teams and measure success.
• Design, build and ship LLM-powered features and agents from prototype to production.
• Own retrieval and RAG over data, collaborating with data engineers.
• Build evals and monitoring for quality, cost, and latency; iterate improvements.
• Help refine customer-facing AI features.

Skills

LLM prod experience

Python

Node.js/TS

Prompt design

RAG retrieval

Agent orchestration

AI eval/monitoring

AWS Bedrock

CI/CD

English fluency

Tools

Terraform

ML tooling

Job description

Project description

Our client is an energy solutions provider. Our AI-enabled platform supports operations, tracking and decision-making to optimize large-scale renewable and traditional energy portfolios. We're delivering a key client MVP in February 2027, and the project continues into 2028. Our stack: Ruby on Rails, Node.js/TypeScript and Python services, PostgreSQL, Snowflake and AWS. We're looking for an applied AI engineer to build AI with real, measurable benefit across our operations and products.

Responsibilities

• Work closely with product teams to help define and execute technical criteriaFind high-value uses for AI and define how success is measuredDesign, build and ship LLM-powered features and agents from prototype to production, such as automating client data onboardingOwn retrieval and RAG over our data, building on the foundation our data engineers prepareBuild evals and monitoring that track quality, cost and latency, and improve systems based on the resultsHelp refine our customer-facing AI features

SKILLS

Must have

• Mid-to-senior software engineer who has shipped LLM-based features or agents to production, not only prototypesProduction experience in Python (preferred) or Node.js/TypeScriptBuilding with LLMs: prompt and tool design, structured outputs, retrieval/RAG and agent orchestrationEvaluating AI systems: eval sets built from representative data, automated graders, human review and production monitoringAWS or another enterprise cloud platform, including its AI services (AWS Bedrock preferred)AI-native development: daily use of agentic AI coding tools, and experience scaling shared AI workflows across a team and into the development lifecycle, including CI/CDStrong English and communication skills

Nice to have

Agent tool protocols such as MCPSQL and cloud warehouse data (Snowflake preferred)Infrastructure as code (e.g. Terraform)Exposure to energy, utilities or sustainability data