The Role
Leads the design, development, deployment, and monitoring of production-grade AI systems. Builds RAG pipelines, generative AI and conversational agents, multi-agent orchestration, automation workflows, forecasting models, and anomaly detection solutions. Owns the full AI lifecycle from data preparation through deployment and evaluation while optimizing performance, fairness, robustness, and explainability. Collaborates with technical and product teams to deliver AI-powered business solutions.
Summary Generated by Built In
Senior AI Engineer
Role Overview
- Lead the design, development, and deployment of advanced AI systems
- Build cutting-edge solutions across machine learning, NLP, generative AI, LLMs, and multi-agent orchestration
- Drive innovation across our product portfolio by solving real-world problems
Key Responsibilities
- Architect, build, and deploy production-grade AI/ML systems at scale
- Design and develop RAG pipelines, agentic AI systems, and multi-agent orchestration solutions
- Build intelligent automation flows and conversational AI agents using frameworks like LangGraph, LangChain, and Microsoft Copilot Studio
- Develop time-series forecasting and anomaly detection models for real-world business use cases
- Apply advanced prompt engineering and integrate Model Context Protocol (MCP) to connect AI agents with enterprise systems
- Own the full AI project lifecycle — from data ingestion and preprocessing through model training, evaluation, deployment, and monitoring
- Collaborate with data scientists, software engineers, and product managers to translate business needs into AI-powered solutions
- Optimize model performance and ensure robustness, fairness, and explainability
- Stay current with the latest AI/ML research and bring relevant advancements into our stack
Our Technology Landscape
- Retrieval-Augmented Generation (RAG) pipelines
- Agentic AI frameworks (e.g., LangGraph, LangChain)
- Multi-agent orchestration systems
- Model Context Protocol (MCP) integrations
- Advanced prompt engineering
- Time-series modeling, forecasting, and anomaly detection
- Intelligent automation flows and workflow orchestration
- Microsoft Copilot Studio for building and deploying custom copilots and conversational AI agents
Required Qualifications
- Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field (PhD preferred)
- 5+ years of AI/ML engineering experience with a proven track record of shipping models to production
- Proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, etc.
- Experience with cloud platforms (AWS, Azure) and MLOps tools
- Strong understanding of data structures, algorithms, and software engineering principles
- Solid grounding in software engineering best practices and system design
- Experience designing and building automation workflows or process automation systems
- Excellent problem-solving and communication skills
Skills Required
- Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field
- PhD in a relevant field
- 5+ years of AI/ML engineering experience
- Proven track record of shipping machine learning models to production
- Proficiency in Python
- Proficiency with machine learning libraries such as TensorFlow, PyTorch, or Scikit-learn
- Experience with cloud platforms such as AWS or Azure
- Experience with MLOps tools
- Strong understanding of data structures, algorithms, and software engineering principles
- Experience with software engineering best practices and system design
- Experience designing and building automation workflows or process automation systems
- Excellent problem-solving and communication skills
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The Company
What We Do
Anblicks is a Cloud Data Analytics Company based out of Dallas, TX, with offices in USA, India, and Australia. Since 2004, Anblicks has been helping customers by bringing value to their data and implementing modern data architecture and advanced analytics solutions in the cloud.
