Backend ML Engineer

Job Description:

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  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.

• 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
• 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

• 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, Tru Lens, Docling, custom harnesses) is needed.
• Familiarity with fine-tuning, LoRA/PEFT, or model distillation is appreciated.
• Experience with MLOps tooling (MLflow, Weights & Biases, Kubeflow) or LLM orchestration frameworks (Lang Chain, Llama Index, Haystack, or custom orchestrators) is a plus.

• 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…