Senior AI Software Developer

Posted 4 Hours Ago
Be an Early Applicant
2 Locations
Remote or Hybrid
Senior level
Artificial Intelligence • Cloud • Information Technology • Consulting
The Role
Owns end-to-end delivery of AI features, including LLM integrations, RAG pipelines, agents, model evaluation, data and retrieval pipelines, MLOps, microservices, observability, security, and performance optimization. Builds production-ready AI systems using Python, containers, Kubernetes, vector databases, and CI/CD. Collaborates with cross-functional teams, reviews designs and code, mentors engineers, and ensures reliability, compliance, and scalability.
Summary Generated by Built In
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

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.

Education and Experience Required:

  • 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

#LI-Hybrid

Additional Skills:

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

What We Can Offer You:

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.

Let's Stay Connected:

Follow @HPECareers on Instagram to see the latest on people, culture and tech at HPE.

#puertorico#networking

Job:

Engineering

Job Level:

TCP_04

    

    

HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer. We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together. Please click here: Equal Employment Opportunity.

Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.

   

HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.

   

No Fees Notice & Recruitment Fraud Disclaimer

 

It has come to HPE’s attention that there has been an increase in recruitment fraud whereby scammer impersonate HPE or HPE-authorized recruiting agencies and offer fake employment opportunities to candidates.  These scammers often seek to obtain personal information or money from candidates.

 

Please note that Hewlett Packard Enterprise (HPE), its direct and indirect subsidiaries and affiliated companies, and its authorized recruitment agencies/vendors will never charge any candidate a registration fee, hiring fee, or any other fee in connection with its recruitment and hiring process.  The credentials of any hiring agency that claims to be working with HPE for recruitment of talent should be verified by candidates and candidates shall be solely responsible to conduct such verification. Any candidate/individual who relies on the erroneous representations made by fraudulent employment agencies does so at their own risk, and HPE disclaims liability for any damages or claims that may result from any such communication.

Skills Required

  • Bachelor's or master's degree in computer science, engineering, data science, machine learning, artificial intelligence, or a closely related quantitative discipline
  • Typically 7-10 years of professional experience
  • Experience with LLMs, agents, prompt engineering, function/tool calling, orchestration frameworks, and RAG
  • Experience with model evaluation metrics, error analysis, and automated evaluation pipelines
  • Experience with embeddings, similarity search, chunking, reranking, and vector database operations
  • Backend development experience with Python and FastAPI or Flask
  • Experience with Docker, Kubernetes, CI/CD, artifact management, and GPU scheduling
  • Experience with metrics, logging, tracing, dashboards, and observability
  • Experience with PyTorch or TensorFlow and Hugging Face
  • Experience with Pandas, SQL or NoSQL, Parquet or Arrow, and Kafka or queues
  • Good networking knowledge
  • Experience with GitHub Actions or Azure DevOps and MLflow or Weights & Biases

Hewlett Packard Enterprise Compensation & Benefits Highlights

  • Parental & Family Support Parental leave is frequently highlighted as a standout, with extended fully paid time off and transition support for new parents. Additional offerings like backup childcare and family-care programs strengthen the family-friendly profile.
  • Retirement Support Retirement programs, including employer-supported 401(k) matching, are consistently cited as solid components of the package. Feedback suggests these offerings contribute meaningful long-term financial support.
  • Wellbeing & Lifestyle Benefits Work-life and wellness features such as Wellness Fridays, flexible/hybrid work, and volunteer time off are commonly emphasized. Feedback suggests these elements enhance balance and day-to-day experience.

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The Company
HQ: Houston, TX
85,422 Employees
Year Founded: 2015

What We Do

In 1939, Bill Hewlett and Dave Packard, college friends turned business partners, started the original Silicon Valley startup in the space of a rented Palo Alto garage. Starting with audio oscillators, the friends built the foundation for a company that would grow to become a global leader in enterprise technology. More than 75 years later, our success is exemplified through our employees’ drive to advance ideas that bring meaningful innovations to life for our customers and partners around the globe. We are guided by our mission to help customers use technology to turn ideas into value, and empower them to transform industries, markets and lives. We simplify Hybrid IT, power the Intelligent Edge and provide the expertise to make it all happen.

Hewlett Packard Enterprise Offices

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