Associate Consultant, Generative AI

Sia•New York, NY

20h•$80,000 - $110,000•Hybrid

About The Position

We are growing our Generative AI consulting practice and looking for motivated recent graduates to join as GenAI Consultants. You'll work at the intersection of cutting-edge AI and real business problems — helping clients across industries design, build, and deploy LLM-powered solutions that create tangible value. This is a hands-on technical role. You'll contribute to the full lifecycle of GenAI projects: from architecture and prototyping through to production deployment, evaluation, and iteration. We invest heavily in your development, and expect you to do the same.

Requirements

Bachelors Degree Required
Recent graduate (within 2 years) in CS, Engineering, Data Science, or equivalent
Strong Python programming skills
Familiarity with at least one GenAI framework (LangChain, LlamaIndex, or similar)
Foundational knowledge of NLP concepts, vector embeddings, and semantic search
Exposure to cloud platforms (AWS, Azure, or GCP)
Ability to explain technical concepts clearly to diverse audiences
Claude Certified Architect (CCA) Requirement: All candidates must hold — or demonstrate the clear ability to obtain within one week of hire — the Claude Certified Architect – Foundations (CCA-F) certification from Anthropic.

Nice To Haves

Masters Degree Preferred
Prior experience building agentic AI systems (LangGraph, AutoGen, etc)
Familiarity with vector databases (Pinecone, Chroma, pgvector, OpenSearch)
Experience with or understanding of the Model Context Protocol (MCP)
Hands-on fine-tuning experience with open-source LLMs
Familiarity with Docker, Git, and CI/CD workflows

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).

Benefits

Competitive Compensation
Annual Base Salary Range: $80,000-110,000 annually, commensurate with experience and qualifications
Annual performance based discretionary bonus
Robust Health Coverage
3 Medical plans
Dental and Vision
Life, AD&D and other voluntary insurance
Tax-Advantaged Accounts
401K retirement plan
4% matching and 100% vested upon enrollment
Health Savings Account (HSA)
Flexible Spending Account (FSA)
Health, Dependent Care, Commuter
Family Friendly Benefits
100% paid parental leave for all new parents with eligible tenure
Building Healthy Families program if enrolled through Medical plan
Time Off to Recharge
Generous Paid Time Off (PTO) policy
9 company holidays plus 1 floating holiday
Extras that Make Life Easier
College savings and student loan repayment assistance
Monthly cell phone stipend
Access to wellness programs at no cost if enrolled through Medical plan, including:
Employee Assistance Program at no cost