Senior AI Software Developer

Hewlett Packard Enterprise • San Juan, PR

10h • Hybrid

About The Position

Senior AI Software Developer This role has been designed as ‘Hybrid’ with an expectation that you will work on average 2 days per week from an HPE office. Who We Are: Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. Job Description: The Senior AI Engineer owns end-to-end delivery of AI features—from design to production—while raising the engineering bar through code quality, reliability, and mentoring. The engineer will convert architecture into robust implementations, proactively manage risks, and ensure observable, secure, and performant AI systems. Important to have Good Networking knowledge

Requirements

• Bachelor's or master’s degree in computer science, engineering, data science, machine learning, artificial intelligence, or closely related quantitative discipline.
• Typically, 7-10 years’ experience.
• LLMs & Agents: Prompt engineering, function/tool calling, orchestration frameworks, RAG.
• ML/DS: Evaluation metrics (precision/recall, BLEU/ROUGE where relevant), error analysis.
• Data/RAG: Embeddings, similarity (cosine/IP), chunking, rerankers, vector DB operations.
• Backend: Python (FastAPI/Flask), microservices patterns.
• MLOps/Infra: Docker, Kubernetes, CI/CD, artifact management, GPU scheduling.
• Observability: Metrics/logging/tracing, dashboards, automated evaluation pipelines.
• Frameworks: PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex.
• Data: Pandas, SQL/NoSQL, Parquet/Arrow, Kafka/queues.
• Vector DBs: FAISS, Milvus, pgvector, Pinecone, Weaviate.
• Ops: GitHub Actions/Azure DevOps, MLFlow/W&B
• Good Networking knowledge

Nice To Haves

• Artificial Intelligence Technologies
• Cross Domain Knowledge
• Data Engineering
• Data Science
• Design Thinking
• Development Fundamentals
• Full Stack Development
• IT Performance
• Machine Learning Operations
• Scalability Testing
• Security-First Mindset

Responsibilities

• Solution Engineering & Delivery Translate high-level designs into clear component contracts, APIs, and service boundaries.
• Implement LLM integrations, RAG pipelines, agents, tool/function calling, and prompt strategies.
• Own feature delivery for sprints/releases; maintain high code quality and documentation.
• Modeling & Evaluation Fine-tune models when needed; design evaluation harnesses and metrics.
• Build A/B testing setups; track accuracy, latency, robustness, and task success rates.
• Conduct error analysis; iterate using feedback efficacy loops and prompt refinement.
• Data & Retrieval Engineering Build ETL/ELT pipelines; curate datasets with metadata, lineage, and validation.
• Implement vector indexing (chunking, embeddings, reranking), tune chunk size & overlap.
• Enforce data governance: PII handling, redaction, consent, auditability.
• MLOps & Platform Readiness Containerize workloads (Docker); orchestrate deployments (Kubernetes/Helm).
• Own CI/CD for ML: train → evaluate → package → deploy → monitor → rollback.
• Maintain model/agent registries, experiment tracking, and reproducible environments.
• Software Engineering & Integration Build microservices and async inference paths; support batch/stream processing.
• Integrate with enterprise auth, observability, telemetry, and logging.
• Write unit/integration/e2e tests, performance benchmarks, and failure-injection tests.
• Observability, Reliability & Performance Instrument with metrics/logs/traces; define SLOs (latency, throughput, error rate).
• Optimize inference: batching, caching (KV cache), quantization, token efficiency.
• Implement guardrails (safety filters, jailbreak detection), auto-evals and alerts.
• Security & Compliance Apply secure coding practices; manage secrets, encryption, and least privilege.
• Ensure compliance (data residency, consent, audit trails); respect IP policies.
• Enforce policy-based access and content safety in user-facing features.
• Collaboration & Mentoring Review designs/PRs; coach L3 engineers on best practices.
• Coordinate with AI Architects, Data Engineers, QA, and Product.

Benefits

• Health & Wellbeing We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.
• Personal & Professional Development We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.
• Unconditional Inclusion We are unconditionally inclusive in the way we work and celebrate individual uniqueness. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level