Forward Deployed AI Engineer (Java experience)

We're looking for a Forward Deployed AI Engineer who combines strong software engineering fundamentals with hands-on AI/LLM engineering and a customer-first mindset. You'll embed with customers and internal partner teams to design, build, and ship production-grade, AI-powered solutions - from intelligent integrations to LLM-driven agents and services - translating real-world business problems into robust, scalable systems. This is a role for engineers who love writing strong Java code, building with modern AI, and working shoulder-to-shoulder with the people using what they build. You won't be handed a spec and left alone: you'll uncover requirements, make architectural calls, wire up AI capabilities that actually work in production, and own the outcome end to end. Location : Remote in LATAM. Working hours are based on the US Central or Eastern Time Zone. About the Company : Abstra is a fast-growing, Nearshore Tech Talent services company, providing top Latin American tech talent to U.S. companies and beyond. Founded by U.S.-bred engineers with over 15 years of experience, Abstra specializes in sourcing skilled professionals across a wide range of technologies to meet our clients’ needs, driving innovation and efficiency. What You'll Do Build production services in Java — design and implement microservices (Spring Boot) that integrate customer systems with our platform and AI capabilities. Engineer AI-powered features — build LLM/GenAI applications using model APIs (Anthropic Claude, OpenAI, AWS Bedrock, etc.): RAG pipelines, agentic workflows, tool/function calling, prompt engineering, and evaluation. Deploy and operate on the cloud — build, ship, and run services and AI workloads on AWS, containerized and orchestrated with Kubernetes. Embed with customers/partners — work directly with client teams to gather requirements, design AI solutions, and drive them to go-live — including responsibly setting expectations about what AI can and can't do. Own the full lifecycle — discovery, design, implementation, evaluation, deployment, and post-launch support of both services and AI features. Make AI production-ready — handle the hard parts: grounding/hallucination control, latency and cost optimization, guardrails, observability, and evaluation/testing of non-deterministic systems. Debug across the stack — diagnose issues spanning distributed systems, APIs, data pipelines, model integrations, and infrastructure. Translate ambiguity into architecture — turn loosely-defined business needs into clear technical designs; push back when there's a better or safer approach. Improve the platform — feed field learnings back into the product; build reusable AI patterns, tooling, prompts, and documentation. What You'll Need (Required) 3+ years of professional software engineering experience with strong Java (Java 8–21). Hands-on experience with Spring Boot and building/consuming RESTful APIs. Applied AI/LLM engineering experience — you've built and shipped something real with LLMs: e.g. RAG, agents, tool calling, prompt engineering, or model-API integration (Claude, OpenAI, Bedrock, Gemini, or similar). Exposure to AWS — deploying and running applications using core services (EC2, S3, IAM, RDS, Lambda, CloudWatch); familiarity with AWS AI/ML services ( e.g. Bedrock, SageMaker) a strong plus. Exposure to Kubernetes — deploying, running, and troubleshooting containerized workloads (Docker + K8s). Solid grasp of relational databases (SQL); familiarity with vector databases / embeddings for retrieval. Strong debugging and problem-solving skills across distributed and AI-integrated systems. Excellent communication — comfortable working directly with customers and non-technical stakeholders, including explaining AI capabilities and limitations. Bachelor's degree in Computer Science or equivalent practical experience. Nice to Have (Preferred) Experience with Kotlin or Python (common for AI/ML tooling); JVM build tools (Maven / Gradle). Agentic frameworks / orchestration — LangChain , LlamaIndex , Spring AI, Model Context Protocol (MCP), or similar. LLM evaluation & observability — building eval harnesses, prompt/version management, tracing ( LangSmith , Langfuse , or homegrown). Classic ML / MLOps — model training, fine-tuning, feature stores, model serving, and deployment pipelines. CI/CD and infrastructure-as-code (Terraform, Helm, CloudFormation). Event-driven / streaming systems (Kafka, SQS/SNS) and microservices patterns. Awareness of responsible AI — safety, guardrails, PII handling, prompt-injection defense , and data privacy. Prior customer-facing / consulting / implementation engineering experience. What We Offer: Flexible working hours and remote work options. Opportunities for professional growth and development. A collaborative and inclusive work environment. The chance to work on impactful projects with a talented team. Excellent compensation in USD. Hardware and software setup.