Senior AI Software Developer

HPE•San Juan, PR

54d•Hybrid

About The Position

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

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.