Generative AI Consultant

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.
• 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).

About Sia

Qualifications

• 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.
• 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.
• 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.
• 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.

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).
• Education: