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Responsibilities
- Translate complex AI concepts into business-friendly language.
- Deliver measurable business outcomes using AI.
- Troubleshoot and analyze AI systems.
- Communicate and document AI solutions effectively.
- Collaborate within an agile team environment.
- Interact with senior stakeholders and architecture groups.
Requirements
- 5–12 years of experience in AI/ML/NLP/LLM engineering or AI consulting.
- Strong hands-on experience with Generative AI, LLMs, prompt engineering, embeddings, RAG pipelines.
- Proficiency with Python, ML frameworks (PyTorch/TensorFlow), and AI cloud platforms (Azure, AWS, GCP).
- Experience with Azure OpenAI / OpenAI API, vector databases (Pinecone, FAISS, Chroma), LangChain, LlamaIndex.
- Understanding of MLOps/AIOps, CI/CD pipelines, and model lifecycle management.
- Familiarity with data engineering, structured/unstructured data processing, and analytics tooling.
- Consulting & Business Skills.
- Ability to translate complex AI concepts into business-friendly language.
- Proven track record delivering measurable business outcomes using AI.
- Strong analytical and troubleshooting skills.
- Excellent communication and documentation abilities.
- Ability to work in an agile, collaborative environment.
- Experience interacting with senior stakeholders and architecture groups.
Skills
- Generative AI
- LLMs
- Prompt Engineering
- Embeddings
- RAG Pipelines
- Python
- PyTorch
- TensorFlow
- Azure OpenAI
- OpenAI API
- Pinecone
- FAISS
- Chroma
- LangChain
- LlamaIndex
- MLOps
- AIOps
- CI/CD Pipelines
- Model Lifecycle Management
- Data Engineering
- Structured Data Processing
- Unstructured Data Processing
- Analytics Tooling
- Consulting
- Business Acumen
- Analytical Skills
- Troubleshooting Skills
- Communication Skills
- Documentation Skills
- Agile Methodologies
- Stakeholder Management
Experience Level
- 5-12 years