Backend ML Engineer

Sterling Computers Corporation•North Sioux City, SD

2d•Onsite

About The Position

Sterling Computers is a technology company that provides IT solutions to a variety of clients, including the federal government, state and local governments, education, and commercial entities. Sterling's Strategic Technologies Group is responsible for learning and becoming subject matter experts in new and emerging technologies. Our team uses this expertise to broaden the portfolio of products and solutions that the company sells, delivers, and manages. Our engineers work on a range of AI-integrated systems, from production RAG platforms and LLM orchestration layers to digital human solutions and intelligent automation pipelines. We are looking for a Backend ML Engineer who is interested in taking AI/ML systems from prototype to production, designing inference APIs, building retrieval and orchestration pipelines, integrating large language models, and operating ML infrastructure at scale. If you thrive in a collaborative, client-focused environment and enjoy shipping AI features that real users depend on, we'd love to have you on our team.

Requirements

3–5 years of experience in backend or ML engineering
Strong working knowledge of Python, including FastAPI or Flask
Experience with modern ML libraries such as PyTorch, Hugging Face Transformers, and sentence-transformers
Proficiency with cloud platforms including AWS, GCP, or Azure
Hands-on experience integrating LLMs (OpenAI, Anthropic, Gemini, or open-source models) into production systems
Familiarity with vector databases such as Weaviate, pgvector, Pinecone, or similar
Experience with retrieval-augmented generation (RAG) patterns
Self-motivated with a positive and professional attitude
Bachelor’s degree in Computer Science, Machine Learning, or a related field (minimum requirement), or equivalent practical experience
Strong experience building backend services with Python (FastAPI/Flask); comfort working with async APIs and request/response patterns for ML inference workloads.
Hands-on experience integrating LLMs and embedding models into production applications, including prompt engineering, context management, and handling rate limits, retries, and streaming responses.
Familiarity with RAG architectures: chunking strategies, embedding pipelines, vector search, reranking, and evaluation metrics (Recall@k, MRR, faithfulness, answer relevance).
Experience with vector databases (Weaviate, pgvector, Pinecone, Qdrant, or similar) and traditional databases (PostgreSQL, MariaDB) for hybrid retrieval and metadata filtering.
Cloud experience (AWS/GCP/Azure) for deploying ML services — including managed inference endpoints, GPU instances, or serverless model hosting.
Strong understanding of API authentication, secure handling of model inputs/outputs, and PII/PHI-aware design where applicable.
Experience with ML observability: tracking latency, token usage, cost-per-query, retrieval quality, and model drift in production.
Background in data pipelines, document ingestion/parsing, or evaluation frameworks (Ragas, TruLens, Docling, custom harnesses) is needed.

Nice To Haves

Knowledge of additional languages such as Node.js, JavaScript, or other relevant languages is a plus
Graduate-level coursework or specialization in ML/AI is a plus
Relevant cloud certifications are a plus
Demonstrated experience shipping ML systems to production is a plus
US DoD Clearance preferred or willingness to obtain such
Familiarity with fine-tuning, LoRA/PEFT, or model distillation is appreciated.
Experience with MLOps tooling (MLflow, Weights & Biases, Kubeflow) or LLM orchestration frameworks (LangChain, LlamaIndex, Haystack, or custom orchestrators) is a plus.

Responsibilities

Build, test, and maintain production ML services — inference APIs, retrieval pipelines, orchestration layers, and guardrail/evaluation components.
Design scalable RESTful and streaming APIs that serve ML model outputs reliably under real-world load.
Integrate and tune LLMs, embedding models, and rerankers; evaluate trade-offs across hosted (Anthropic, OpenAI, Vertex) and self-hosted (HF, vLLM) options on cost, latency, and quality.
Build ingestion and chunking pipelines for unstructured data (PDFs, HTML, transcripts) and maintain vector store schemas for multi-tenant or multi-domain retrieval.
Implement evaluation harnesses to measure retrieval quality, generation faithfulness, and end-to-end answer correctness; close the loop from evals back into pipeline improvements.
Containerize and deploy ML workloads with Docker and Kubernetes; manage GPU/CPU resource allocation and model versioning.
Optimize database queries, vector search performance, and caching strategies (including LLM prompt caching) to reduce latency and cost.
Implement CI/CD pipelines for ML services and instrument monitoring for both system metrics (latency, error rate) and ML-specific metrics (retrieval quality, hallucination rate, drift).
Collaborate with frontend engineers, ML researchers, and product analysts to translate model capabilities into shipped features.
Document backend and ML infrastructure, including model cards, evaluation results, and architectural decisions