Forward Deployed AI Engineer

Requirements

8+ years of professional software development experience, with a deep understanding of full-stack architectures.

Hands-on experience delivering production AI/ML systems, including generative AI or agentic AI applications.

Strong proficiency with AI coding tools such as Claude Code, Cursor, Codex, or GitHub Copilot.

Deep understanding of agentic architecture patterns, including single- and multi-agent workflows, supervisor/worker patterns, state and memory management, orchestration, and safe action execution.

Strong knowledge of modern GenAI patterns, including advanced RAG, embeddings, vector and hybrid search, reranking, tool/function calling, structured outputs, and context engineering.

Proficiency in Python and AI engineering frameworks such as LangChain, LangGraph, LlamaIndex, or equivalent.

Working knowledge of cloud AI/ML platforms such as Amazon Bedrock, Azure AI Foundry, and Vertex AI.

Familiarity with enterprise data platforms such as Snowflake, Databricks, and vector databases.

Experience establishing LLMOps/MLOps practices, including versioning, CI/CD, containerization, and cost and performance optimization.

Ability to structure codebases and specifications to produce reliable, context-efficient AI outputs.

Strong judgment in architecture trade-offs, model selection, retrieval strategies, latency, cost, security, and vendor lock-in.

Ability to explain technical decisions clearly to technical and non-technical stakeholders.

Comfortable engaging with executives and frontline users.

Strong attention to detail, ownership, problem-solving, and a proactive mindset.

Experience working with Agile/Scrum teams and large, complex systems.

Responsibilities

Assess AI feasibility and define technical direction for AI initiatives, including data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, and monitoring.

Design and build LLM workflows, retrieval systems, and agentic applications using enterprise data and infrastructure.

Define secure, scalable architectures across cloud and AI platforms.

Establish evaluation and observability standards, including automated evaluations, tracing, quality scoring, latency and cost monitoring, and regression testing.

Implement human-in-the-loop controls and sound data security practices.

Develop prototypes that can evolve into production systems and transfer technical knowledge to teams responsible for ongoing maintenance and scaling.

Evaluate emerging models, frameworks, and tools, recommending practical solutions based on business needs.

Optimize AI systems for reliability, security, performance, and cost efficiency.

Collaborate with technical teams, executives, and end users to align AI solutions with business objectives.

Support knowledge transfer and help teams develop the capabilities needed to maintain and scale AI solutions.

Nice to Have

Experience implementing AI solutions across different enterprise environments and technology stacks.

Experience transferring AI solutions to internal teams and supporting their adoption and scaling.

Familiarity with advanced AI evaluation frameworks, observability platforms, and automated quality assessment tools.

Experience establishing AI engineering practices in environments with limited existing infrastructure or governance.

Benefits

At Devsu, we believe in creating an environment where you can thrive both personally and professionally. By joining our team, you'll enjoy:

A stable, long-term contract with opportunities for professional growth.

Private health insurance.

A remote culture that promotes work-life balance.

Ongoing training, mentoring, and learning programs to keep you at the forefront of the industry.

Free access to AI training resources and cutting-edge AI tools to enhance your daily work.

Flexible paid time off (PTO) and paid holidays.

Challenging, world-class software projects for clients in the U.S. and Latin America.

Collaboration with talen