About The Position
We are seeking a Lead AI Architect to design, develop, and deploy enterprise-scale AI and Generative AI solutions. The ideal candidate will have expertise in LLM-powered applications, agentic AI, RAG architectures, traditional machine learning, and cloud-based AI platforms, while providing technical leadership across cross-functional teams.
Requirements
- Bachelor's degree in Computer Science, Mathematics, Statistics, or a related field (or equivalent experience).
- 3+ years of experience in AI/ML, including deep learning, NLP, supervised and unsupervised learning.
- Proven experience building and deploying LLM-powered applications, RAG pipelines, agent architectures, and evaluation frameworks.
- Hands-on experience with LangChain, LlamaIndex, CrewAI, AutoGen, and Hugging Face Transformers.
- Strong Python programming skills with experience in Pandas, PySpark, TensorFlow, and XGBoost.
- Experience developing production-grade RESTful APIs and microservices.
- Experience with Azure ML, Vertex AI, and vector databases such as Pinecone, Weaviate, Milvus, or FAISS.
- Knowledge of Docker, Kubernetes, and modern AI deployment practices.
- Experience with prompt tuning, fine-tuning, LoRA, and PEFT methodologies.
- Familiarity with knowledge graphs and multi-agent AI systems.
- Excellent communication, presentation, and stakeholder management skills.
Responsibilities
- Design, develop, and deploy production LLM applications utilizing prompt engineering, context engineering, RAG, and agent orchestration.
- Architect and implement agentic workflows using LangChain, LlamaIndex, CrewAI, AutoGen, and Hugging Face Transformers.
- Integrate LLMs with knowledge graphs and multi-agent systems to solve complex business problems.
- Build scalable RESTful APIs, microservices, and vector database solutions (Pinecone, Weaviate, Milvus, FAISS).
- Apply AI/ML techniques including deep learning, supervised and unsupervised learning, NLP, NER, text classification, and sentiment analysis.
- Develop model evaluation frameworks to measure AI quality, safety, and performance.
- Implement model adaptation techniques including prompt tuning, LoRA, and PEFT.
- Leverage cloud platforms (Azure ML, Vertex AI) and container technologies (Docker, Kubernetes) to deploy and scale AI solutions.
- Collaborate with business and technical stakeholders to align AI initiatives with organizational goals.
- Document AI architectures, deployment patterns, and best practices.
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
Upload and Match Resume
What This Job Offers
Job Type
Full-time
Career Level
Senior
