Note: The job is a remote job and is open to candidates in USA. Dice is seeking an experienced Senior Data Scientist / AI-ML Engineer to design, develop, deploy, and optimize scalable AI and machine learning solutions. The role involves building and maintaining AI applications, collaborating with cross-functional teams, and ensuring responsible AI practices are implemented.
Responsibilities
- Design, build, train, fine-tune, evaluate, and deploy Machine Learning, Deep Learning, and Generative AI models into production environments
- Develop and implement LLM-powered applications using OpenAI, Claude, Llama, Gemini, and other foundation models
- Build RAG pipelines using LangChain, LangGraph, LlamaIndex, vector databases, and embedding models for enterprise knowledge retrieval
- Design and implement Agentic AI and multi-agent systems with autonomous planning, tool orchestration, memory, and workflow automation
- Develop scalable feature engineering pipelines, ML pipelines, feature stores, model serving, and inference APIs
- Build and maintain MLOps/LLMOps pipelines for model training, versioning, deployment, monitoring, drift detection, and automated retraining
- Collaborate with cross-functional teams to integrate AI capabilities into customer-facing applications, APIs, and enterprise platforms
- Translate complex business requirements into scalable AI/ML solutions with measurable business outcomes
- Optimize AI models for performance, latency, scalability, security, and cost efficiency
- Implement responsible AI practices including model evaluation, guardrails, governance, explainability, and monitoring
Skills
- Strong programming experience in Python and SQL
- Hands-on experience with Machine Learning, Deep Learning, NLP, Computer Vision, and predictive analytics
- Strong experience with PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, and Hugging Face Transformers
- Expertise in Generative AI, LLMs, Prompt Engineering, RAG, AI Agents, MCP, Function Calling, and Multi-Agent Architectures
- Experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar AI orchestration frameworks
- Hands-on experience with Vector Databases such as Pinecone, FAISS, ChromaDB, Weaviate, or PGVector
- Experience deploying AI workloads on AWS (SageMaker, Bedrock, Lambda, ECS, EKS, S3), Azure AI, or Google Vertex AI
- Experience with MLflow, Kubeflow, SageMaker Pipelines, Model Registry, CI/CD, and model monitoring
- Knowledge of Docker, Kubernetes, REST APIs, Git, Terraform, and cloud-native architectures
- Experience in Healthcare, Financial Services, Retail, Manufacturing, or Insurance domains
- Experience building enterprise-scale GenAI, RAG, Conversational AI, AI Copilots, and Intelligent Automation solutions
- Knowledge of AI governance, security, compliance, and Responsible AI best practices
- AWS, Azure, or Google Cloud AI/ML certifications are preferred
Company Overview