Excelgens
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About Agadia
We specialize in healthcare management technology and offer our clients a suite of utilization management software solutions. Our mission is to improve the quality of care and expedite the delivery of healthcare services by advancing and automating utilization management processes. We are based in Parsippany, NJ, and have clients across the United States.
Key Responsibilities: Strategic AI Architecture
Design, architect, and oversee implementation of end-to-end AI systems, spanning data ingestion, model development, evaluation, deployment, and observability. Lead architecture for agentic systems with memory, planning, and tool-use capabilities. Build Hybrid AI Architectures That Integrate
Traditional ML (XGBoost, SVM, Random Forest) Deep Learning (CNNs, RNNs, Transformers) Generative AI (LLMs, Diffusion Models, Multimodal AI) Generative AI & Agentic Systems Develop Applications Using LLMs (GPT-4/Claude/Gemini, Etc.) With Frameworks Like
LangChain, LlamaIndex, Haystack Vector DBs (Weaviate, Pinecone, FAISS, Qdrant) Architect RAG pipelines, prompt engineering workflows, and tool-using agents (AutoGPT-style). Optimize inference, memory management, and token budgeting for agent runtimes. Traditional AI/ML & Data Science
Guide the development of supervised and unsupervised ML models for classification, regression, clustering, forecasting, and anomaly detection. Translate business problems into mathematical formulations and data science models. Collaborate with Data Engineering to optimize pipelines, feature stores, and model-serving infrastructure. Infrastructure & Tooling
Deploy models via cloud-native platforms (AWS Sagemaker, Azure ML, GCP Vertex AI). Use MLOps tools for versioning, CI/CD, drift detection (MLflow, Kubeflow, Arize). Leverage orchestration tools like Airflow or Prefect to manage complex workflows. Leadership & Governance
Mentor junior data scientists and AI engineers across the SDLC. Participate in executive-level planning for AI adoption and roadmap. Define and enforce responsible AI practices: model fairness, privacy, explainability. Qualifications:
Required: Bachelor's or Master's in Computer Science, AI, Data Science, or related field. 8+ years experience in AI/ML, with 3+ years in architecture or leadership roles. Proven delivery of AI systems in production: GenAI + traditional ML. Strong knowledge of LLMs, Transformer models, Vector embeddings, and Agents. Experience with Python (TensorFlow, PyTorch, HuggingFace, Scikit-learn), SQL, and cloud platforms. Preferred: PhD in AI, NLP, or Applied ML. Experience integrating AI into enterprise platforms, decision support tools, or clinical systems. Familiarity with HIPAA, GDPR, or healthcare-specific data compliance (for regulated environments). Soft Skills & Traits:
Strategic thinker with strong communication skills. Natural collaborator who can lead across data, engineering, product, and executive teams. Seniority level
Mid-Senior level Employment type
Full-time Job function
Information Technology Industries
IT Services and IT Consulting
#J-18808-Ljbffr
Design, architect, and oversee implementation of end-to-end AI systems, spanning data ingestion, model development, evaluation, deployment, and observability. Lead architecture for agentic systems with memory, planning, and tool-use capabilities. Build Hybrid AI Architectures That Integrate
Traditional ML (XGBoost, SVM, Random Forest) Deep Learning (CNNs, RNNs, Transformers) Generative AI (LLMs, Diffusion Models, Multimodal AI) Generative AI & Agentic Systems Develop Applications Using LLMs (GPT-4/Claude/Gemini, Etc.) With Frameworks Like
LangChain, LlamaIndex, Haystack Vector DBs (Weaviate, Pinecone, FAISS, Qdrant) Architect RAG pipelines, prompt engineering workflows, and tool-using agents (AutoGPT-style). Optimize inference, memory management, and token budgeting for agent runtimes. Traditional AI/ML & Data Science
Guide the development of supervised and unsupervised ML models for classification, regression, clustering, forecasting, and anomaly detection. Translate business problems into mathematical formulations and data science models. Collaborate with Data Engineering to optimize pipelines, feature stores, and model-serving infrastructure. Infrastructure & Tooling
Deploy models via cloud-native platforms (AWS Sagemaker, Azure ML, GCP Vertex AI). Use MLOps tools for versioning, CI/CD, drift detection (MLflow, Kubeflow, Arize). Leverage orchestration tools like Airflow or Prefect to manage complex workflows. Leadership & Governance
Mentor junior data scientists and AI engineers across the SDLC. Participate in executive-level planning for AI adoption and roadmap. Define and enforce responsible AI practices: model fairness, privacy, explainability. Qualifications:
Required: Bachelor's or Master's in Computer Science, AI, Data Science, or related field. 8+ years experience in AI/ML, with 3+ years in architecture or leadership roles. Proven delivery of AI systems in production: GenAI + traditional ML. Strong knowledge of LLMs, Transformer models, Vector embeddings, and Agents. Experience with Python (TensorFlow, PyTorch, HuggingFace, Scikit-learn), SQL, and cloud platforms. Preferred: PhD in AI, NLP, or Applied ML. Experience integrating AI into enterprise platforms, decision support tools, or clinical systems. Familiarity with HIPAA, GDPR, or healthcare-specific data compliance (for regulated environments). Soft Skills & Traits:
Strategic thinker with strong communication skills. Natural collaborator who can lead across data, engineering, product, and executive teams. Seniority level
Mid-Senior level Employment type
Full-time Job function
Information Technology Industries
IT Services and IT Consulting
#J-18808-Ljbffr