Senior Artificial Intelligence Engineer

Senior AI Engineer Production AI Agents

As an AI Engineer focused on Production AI Agents, you will partner closely with Product,
Research, Engineering, and cross-functional stakeholders to design, build, and scale
AI-powered systems that enhance how insights are generated and operationalized. This role
emphasizes moving beyond experimentation to deliver reliable, evaluation-driven AI solutions that integrate seamlessly into real workflows. You will play a key role in shaping GCI’s AI ecosystem by building robust agent architectures, ensuring production readiness, and continuously improving system performance and trust.

What you will accomplish:

Design and build stateful, multi-agent AI systems using modern orchestration
frameworks, enabling scalable and reliable workflows for insights generation and
synthesis.

Collaborate with Product, Research, and business stakeholders to translate
requirements into end-to-end AI solutions, from proof of concept through evaluation
and production deployment.

Solid understanding of Retrieval-Augmented Generation (RAG), including hybrid
search, re-ranking, and advanced retrieval techniques.
Implement evaluation frameworks and pipelines (e.g., LLM-as-a-Judge, automated
benchmarks) to measure system performance, reliability, and quality before and after
release.
Develop and maintain scalable backend services for high-throughput, low-latency
workloads, while contributing to lightweight frontend components to deliver functional
prototypes and internal tools.
Optimize batching, streaming, caching, and request orchestration in distributed and
async environments.
Improve production systems across latency, throughput, reliability, observability, and unit
economics.
Partner with infrastructure teams to leverage GPU-enabled and cloud-native
environments effectively.
Establish monitoring, tracing, and observability practices for complex AI systems,
ensuring performance, reliability, and debuggability in production.
Develop reusable platform components, MCPs / APIs, and best practices for AI
application development.
Drive a pragmatic, evaluation-driven approach to adopting new AI technologies,
balancing innovation with reliability and business impact.
Stay current with advancements in AI (e.g., reasoning models, SLMs, prompting
strategies) and apply them to improve systems and workflows.
Partner with cross-functional teams to ensure AI solutions align with responsible AI,
privacy, and security standards.

What you will bring:

8 to 12 years of experience in software engineering, AI/ML engineering, or full-stack
development, with hands-on ownership of building and deploying production-grade
applications or platforms.
6+ years of focused experience building and deploying AI-centric systems.
4+ years of hands-on experience with LLM-based agents, autonomous workflows, or
multi-agent orchestration.
Strong full-stack engineering experience, with deep expertise in Python and familiarity
with TypeScript or Node.js.
Hands-on experience with AI orchestration frameworks such as LangGraph,
LlamaIndex Workflows, or similar tools.
Solid understanding of Retrieval-Augmented Generation (RAG), including hybrid
search, re-ranking, and advanced retrieval techniques.
Experience implementing observability and tracing for AI systems (e.g., LangSmith,
LangFuse, Arize Phoenix).
Production experience with modern ML tooling and frameworks (for example: PyTorch,
Transformers, scikit-learn).
Proven experience taking AI-powered products from prototype to production with strong
maintainability and operational quality.
Proven ability to design and execute evaluation pipelines and testing frameworks to
ensure reliability and reduce hallucinations.
Experience working with APIs/SDKs from major model providers (OpenAI, Anthropic,
Gemini) and open-source models.
Experience deploying and managing services on cloud platforms (AWS, Azure, or
GCP) and using containerization (Docker/Kubernetes).
Familiarity with CI/CD pipelines and DevOps practices.
Strong collaboration and communication skills, with the ability to work effectively across
technical and non-technical teams.
Preferred Qualifications:
Experience with Spring-based service development.
Familiarity with big data and processing ecosystems (for example: Spark, Hadoop).
Experience with streaming systems (for example: Kafka, Flink, Beam).
Experience with RAG pipelines, vector stores, tool-use frameworks, and multimodal
model integration.
Exposure to GPU optimization and performance tuning (for example: CUDA, inference
optimization techniques).
Experience building conversational AI systems (intents, entities, dialog flows, and
interaction design).
Familiarity with prompt optimization tools such as DSPy.
Proficiency with vector databases (Pinecone, Weaviate, Qdrant, pgvector).
Exposure to voice agents or multimodal AI systems.
Experience with graph databases (e.g., Neo4j) or GraphRAG approaches.
Foundational knowledge of machine learning or model fine-tuning