Generative AI Consultant
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Key skills for this role
About the Role
Sia seeks a Generative AI Consultant to design and implement cutting-edge GenAI/LLM solutions for diverse industries. You will bridge technical teams and clients, architect AI products using RAG, agents, and fine-tuning.
Key Skills for This Role
Responsibilities
- Design, build, train, fine tune, and deploy sophisticated AI models leveraging LLMs (e.g., GPT x, Claude, Gemini, Llama, Mistral) and other generative techniques
- Support the GenAI Solution Architect in designing robust, scalable, and secure applications
- Develop applications powered by GenAI models that meet business needs and comply with applicable regulations
- Design and optimize effective prompts (e.g., few shot, Chain/Tree/Graph of Thought, ReAct, Self reflection, guardrails)
- Design and implement Retrieval Augmented Generation (RAG) architectures to improve accuracy and relevance
- Select and fine tune appropriate models (including multimodal VLM, SLM) to create higher quality content
- Implement MLOps best practices for the GenAI lifecycle, including automated pipelines (CI/CD), versioning, monitoring, and maintenance in production environments
- Develop and execute rigorous evaluation frameworks to measure model performance, reliability, fairness, and safety
- Stay abreast of the latest advancements in GenAI techniques, technologies, and frameworks
- Work effectively within cross functional teams, communicating complex technical concepts clearly to diverse stakeholders
Requirements
- Master's degree or Engineering degree (or equivalent) in Computer Science, Engineering, Data Science, or a related quantitative field
- 1 3+ years of hands on experience in machine learning, software engineering, or data science, with demonstrated experience on complex AI projects specifically involving LLMs and Generative AI
- Strong programming skills, particularly in Python
- Proficiency in at least one major GenAI application framework (LangChain, LlamaIndex, etc.)
- Proven experience in NLP/NLU, vector embeddings, and semantic search
- Hands on experience with fine tuning LLMs
- Mastery of advanced prompting techniques (Chain/Tree/Graph of Thought, ReAct, etc.)
- Knowledge of both proprietary and open source model ecosystems (OpenAI, Anthropic, Mistral AI, Hugging Face, models hosted on AWS/Azure/GCP, etc.)
- Experience with Cloud platforms (AWS, Azure, or GCP) and their associated MLOps/AI services
- Familiarity with MLOps principles, CI/CD tools, Docker, and Git
Full Job Posting
Job Description
- Join us as an experienced Generative AI Specialist designing and implementing cutting edge GenAI/LLM solutions across diverse industries.
- You'll act as a vital bridge between technical teams (Data Science, ML, Platform) to deliver business centric AI value.
- You'll guide clients on the optimal path—using techniques like RAG, agents, or fine tuning—for cost effective impact.
Responsibilities
- LLM/GenAI System Development: Design, build, train, fine tune, and deploy sophisticated AI models leveraging LLMs (e.g., GPT x, Claude, Gemini, Llama, Mistral) and other generative techniques.
- Assist in Solution Architecture: Support the GenAI Solution Architect in designing robust, scalable, and secure applications.
- Application Development: Develop applications powered by GenAI models (both self managed and API accessible) that meet business needs and comply with applicable regulations (GDPR, EU AI Act, model licenses, etc.).
- Advanced Prompt Engineering: Design and optimize effective prompts (e.g., few shot, Chain/Tree/Graph of Thought, ReAct, Self reflection, guardrails).
- RAG Implementation: Design and implement Retrieval Augmented Generation (RAG) architectures to improve accuracy and relevance.
- Model Selection & Fine Tuning: Select and fine tune appropriate models (including multimodal VLM, SLM) to create higher quality content.
- Integration & Deployment (MLOps): Implement MLOps best practices for the GenAI lifecycle, including automated pipelines (CI/CD), versioning, monitoring, and maintenance in production environments.
- Evaluation & Responsible AI: Develop and execute rigorous evaluation frameworks to measure model performance, reliability, fairness, and safety.
- Research & Innovation: Stay abreast of the latest advancements in GenAI techniques, technologies, and frameworks.
- Collaboration: Work effectively within cross functional teams, communicating complex technical concepts clearly to diverse stakeholders.
- Documentation: Document processes, methodologies, and best practices for knowledge sharing and future reference.
- Use Case Differentiation: Distinguish between use cases suited for Generative AI versus traditional NLP applications.
Qualifications
- Master's degree or Engineering degree (or equivalent) in Computer Science, Engineering, Data Science, or a related quantitative field.
- 1 3+ years of hands on experience in machine learning, software engineering, or data science, with demonstrated experience on complex AI projects specifically involving LLMs and Generative AI.
- Strong programming skills, particularly in Python.
- Proficiency in at least one major GenAI application framework (LangChain, LlamaIndex, etc.).
- Proven experience in NLP/NLU, vector embeddings, and semantic search.
- Hands on experience with fine tuning LLMs.
- Mastery of advanced prompting techniques (Chain/Tree/Graph of Thought, ReAct, etc.).
- Knowledge of both proprietary and open source model ecosystems (OpenAI, Anthropic, Mistral AI, Hugging Face, models hosted on AWS/Azure/GCP, etc.).
- Experience with Cloud platforms (AWS, Azure, or GCP) and their associated MLOps/AI services.
- Familiarity with MLOps principles, CI/CD tools, Docker, and Git.
Preferred Qualifications
- Experience building agentic AI systems (e.g., LangGraph, AutoGen).
- Experience with vector databases (e.g., pgvector, Pinecone, Chroma, OpenSearch).
- Experience developing conversational AI systems / chatbots.
- Familiarity with deep learning frameworks (PyTorch, TensorFlow).
- Understanding of or experience with protocols and standards for connecting LLMs to external tools and databases, such as Model Context Protocol (MCP).
- Experience deploying models at scale in production environments.
Additional Information
- Sia is an equal opportunity employer. All aspects of employment are based solely on performance, competence, conduct, or business needs.
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