AI Software Engineer

Department: Physician Enterprise Development

Location: New Jersey Office

Compensation: $115,000 - $137,500 / year

Description At Hallmark Health Care Solutions, what we do and how we do it go hand in hand. We bring a relentless focus to both our products and our people—grounded in curiosity, a bias for action, accountability and ownership, strong judgment, and a willingness to challenge the status quo in pursuit of better outcomes.

Hallmark Health Care Solutions is led by passionate experts with an average of 20+ years’ experience in IT, nursing, process engineering, finance, and healthcare. We are committed to strengthening communities by producing technologies, support services, and thought leadership that tangibly improve patient care and community health outcomes.

• Please note, this role is NOT 100% remote. We are considering applicants in the Princeton, NJ area who can interview in person and occasionally work in our NJ office.

What You’ll Do We are looking for a highly capable and hands-on AI Engineer to join our growing engineering and product innovation team. The ideal candidate should be able to understand complex business and AI requirements, architect practical AI-driven solutions, collaborate with technical leads and managers, and independently deliver production-grade AI initiatives.

What We Are Looking For

• Someone who can think beyond demos and build AI systems that work reliably in real-world production environments.
• A self-driven engineer who can take ownership of AI initiatives with minimal supervision.
• A strong collaborator who can work effectively with leadership, managers, architects, and distributed engineering teams.
• An individual passionate about solving meaningful business problems using practical AI innovation.

AI Solution Design & Delivery

• Understand business problems and translate them into scalable AI/ML and Generative AI solutions.
• Design, develop, and deploy enterprise-grade AI systems with a strong focus on reliability, scalability, monitoring, and measurable outcomes.
• Lead or contribute to multiple AI initiatives simultaneously while coordinating with engineering leads, product managers, architects, and offshore teams.
• Independently drive proof-of-concepts, pilots, and production implementations.

Agentic AI & LLM Systems

• Design and implement Agentic AI workflows capable of multi-step reasoning, orchestration, task automation, and intelligent decision support.
• Build AI agents using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, MCP, or equivalent orchestration platforms.
• Implement memory management, evaluation pipelines, guardrails, failure recovery, and observability for production AI systems.
• Develop prompt engineering strategies and optimize LLM interactions for enterprise use cases.

RAG & Semantic Search

• Build scalable Retrieval-Augmented Generation (RAG) systems including:
• Document ingestion pipelines
• Chunking strategies
• Embedding generation
• Vector databases
• Hybrid semantic and keyword retrieval
• Re-ranking pipelines
• Citation traceability
• Work with vector stores such as FAISS, Pinecone, Weaviate, or similar technologies.
• Implement evaluation frameworks to measure retrieval quality and response accuracy.

AI/ML Engineering

• Develop and optimize ML and Deep Learning models for predictive analytics, classification, forecasting, NLP, computer vision, and recommendation systems.
• Work with modern ML frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost, and Hugging Face.
• Participate in model fine-tuning, instruction tuning, RLHF, LoRA, and PEFT initiatives where applicable.

Cloud, MLOps & Engineering Excellence

• Build scalable AI infrastructure on cloud platforms such as Azure, AWS, or GCP.
• Implement MLOps best practices including CI/CD pipelines, model monitoring, experiment tracking, infrastructure automation, and deployment orchestration.
• Work with tools such as Docker, Kubernetes, Terraform, MLflow, Airflow, and GitHub Actions.
• Optimize infrastructure usage, performance, and operational cost.

Collaboration & Leadership

• Work closely with onsite and offshore engineering teams to ensure smooth delivery and communication.
• Collaborate with technical leads, architects, QA teams, DevOps teams, and product stakeholders.
• Mentor junior engineers and contribute to AI capability building within the organizatio…