Generative AI Consultant at Sia Partners - Canada

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

30 Jul, 26

Salary

105000.0

Posted On

01 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Generative AI, LLM, RAG, Prompt engineering, Machine learning, MLOps, Cloud platforms, Data science, Fine-tuning, Vector databases, CI/CD, Docker, Git, Natural language processing, Solution architecture

Industry

Business Consulting and Services

Description

Company Description About Sia Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We’re optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity. With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes. Sia’s AI & Data Business Unit is the powerhouse of our firm’s innovation—merging cutting-edge Data Science, Generative AI, and advanced digital solutions to transform industries. With over 350 experts worldwide, we tackle projects from proof-of-concept to large-scale deployment, always pushing the boundaries of AI capabilities. Our 12 R&D labs in Europe and North America drive continuous research in areas like computer vision, MLOps, and deep learning, partnering closely with our business consultants for real-world impact. By joining Sia’s AI & Data team, you’ll step into a vibrant, collaborative environment that nurtures professional growth and empowers you to shape the future of AI-driven consulting. 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. Your responsibilities extend beyond prompting to architecting robust AI products via benchmarking, prototyping, and validation. You'll orchestrate the full AI workflow, ensuring seamless model integration optimized for performance, security, and scale, while tackling infrastructure challenges. We provide extensive training to support your success. If you're driven to push AI boundaries and help clients rapidly adopt GenAI with confidence, apply to make a real difference. 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), balancing simplicity and complexity to enhance analytical capabilities, refine outputs, improve user experience, and control interactions. RAG Implementation: Design and implement Retrieval-Augmented Generation (RAG) architectures to improve accuracy and relevance by retrieving information from pre-determined knowledge sources, providing traceability (source attribution). Model Selection & Fine-Tuning: Select and fine-tune appropriate models (including multimodal - VLM, SLM - Visual Language Models, Small Language Models) to create higher-quality content (text, image, audio, code, etc.) and maximize business value creation opportunities. Integration & Deployment (MLOps): Implement MLOps best practices for the GenAI lifecycle, including automated pipelines (CI/CD), versioning, monitoring, and maintenance in production environments (Cloud platforms like AWS, Azure, GCP). Ensure seamless integration into existing systems and with external tools/APIs, potentially utilizing standardized protocols (MCP). Evaluation & Responsible AI: Develop and execute rigorous evaluation frameworks to measure model performance, reliability, fairness, and safety. Ensure adherence to Responsible AI principles and help teams and clients navigate end-to-end security and compliance processes. Research & Innovation: Stay abreast of the latest advancements in GenAI techniques, technologies, and frameworks. Experiment with new approaches and contribute to internal knowledge sharing. Collaboration: Work effectively within cross-functional teams, communicating complex technical concepts clearly to diverse stakeholders (both technical and non-technical). 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 (e.g., NER, sentiment analysis). Qualifications Education: Master's degree or Engineering degree (or equivalent) in Computer Science, Engineering, Data Science, or a related quantitative field. Experience: 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. Required Technical Skills: 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. Soft Skills: Excellent communication skills (written and verbal), ability to explain complex technical concepts simply. Strong analytical and complex problem-solving abilities. Team player with the ability to collaborate effectively with diverse profiles. Curiosity, pioneering spirit, and a demonstrated commitment to continuous learning. 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 Compensation & Benefits Base salary starting at $105,000, commensurate with experience and qualifications Annual Discretionary Bonus Healthcare coverage that includes 3 medical plan options: Anthem – EPO HSA, EPO HSA MERP, and PPO; dental and vision through MetLife; and life insurance policies through Mutual of Omaha Flexible Spending Account (FSA) Paid Time Off Parental leave paid at 100% of base pay for all new parents 9 Company Holidays + 1 Floating Holiday 401(k) Plan - 4% matching and vested on day 1 College save-up plan & college loan repayment plan Monthly cell phone stipend Pre-tax account for Parking and Mass Transit Sia provides several wellness and incentive programs free of charge through the firm medical plan – Anthem - such as: Gym Reimbursement LiveHealth Online Well-being Coach Building Healthy Families Program And much more Diversity, Equity, Inclusion & Belonging At Sia, we believe in fostering a diverse, equitable and inclusive culture where our employees and partners are valued and thrive in a sense of belonging. We are committed to recruiting and developing a diverse network of employees and investing in their growth by providing unique opportunities for professional and cultural immersion. Our commitment toward inclusion motivates dynamic collaboration with our clients, building trust by creating an inclusive environment of curiosity and learning which affects lasting impact. Please visit our website for more information. Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs. Office Workplace Guidelines Sia is committed to providing a flexible workplace environment that supports client, business, and market needs. Consultants located in our primary market office locations—New York City, Charlotte, Seattle, and San Francisco—are expected to live within a reasonable commuting distance and attend the office at least three days per week. For Consultants outside of our primary markets, we can offer more flexible in-person requirements in accordance with your location. Work Authorization & Sponsorship At this time, Sia does not intend to pursue employment with applicants who will require now or in the future visa by our company for work authorization in the United States (i.e., H1-B visa, F-1 visa (OPT), TN visa, or any other non-immigrant status). Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.

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Responsibilities

You will design, build, and deploy sophisticated Generative AI and LLM solutions while acting as a bridge between technical teams and business stakeholders. Responsibilities include architecting AI products, implementing RAG architectures, and ensuring MLOps best practices for scalable production environments.