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Ml Inference Jobs in Caguas, PR (NOW HIRING)

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Ml Inference information

See Caguas, PR salary details

$36.6K

$119.7K

$191.6K

How much do ml inference jobs pay per year?

As of Aug 18, 2026, the average yearly pay for ml inference in Caguas, PR is $119,685.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,000.00 and $132,600.00 per year, depending on experience, location, and employer.

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

Ml Inference jobs near you

Senior AI Software Developer

Hewlett Packard Enterprise Development LP

San Juan, PR • Hybrid

$53.50 - $70.50/hr

Full-time

Re-posted 13 days ago

Job description

Senior AI Software DeveloperThis 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 onfinding 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.

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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/vendorswill 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.

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