Hewlett Packard Enterprise

Senior AI Software Developer

Hewlett Packard Enterprise

full-time

Posted on:

Location Type: Hybrid

Location: San Juan • Puerto Rico

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Job Level

Senior

Tech Stack

AzureDockerETLFlaskKafkaKubernetesMicroservicesNoSQLPandasPythonPyTorchSQLTensorflow

About the role

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Review designs/PRs; coach L3 engineers on best practices.
  • Coordinate with AI Architects, Data Engineers, QA, and Product.

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.
  • Knowledge and Skills: 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
Benefits
  • Health & Wellbeing
  • Personal & Professional Development
  • Unconditional Inclusion
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
PythonLLMsPrompt engineeringFunction callingRAG pipelinesDockerKubernetesCI/CDPyTorchTensorFlow
Soft Skills
mentoringrisk managementerror analysiscoachingcollaboration