AI Architect

Job Description

About the Company

Impact Analytics (Series D Funded) delivers AI-native SaaS solutions and consulting services that help companies maximize profitability and customer satisfaction through deeper data insights and predictive analytics. With a fully integrated, end-to-end platform for planning, forecasting, merchandising, pricing, and promotions, Impact Analytics empowers companies to make smarter decisions based on real-time insights rather than relying on last year's inputs to forecast and plan this year's business. Powered by over one million machine learning models, Impact Analytics has been leading AI innovation for a decade, setting new benchmarks in forecasting, planning, and operational excellence across the retail, grocery, manufacturing, and CPG sectors. In 2025, Impact Analytics is at the forefront of the Agentic AI revolution, delivering autonomous solutions that enable businesses to adapt in real time, optimize operations, and drive profitability without manual intervention. Here's a link to our website: www.impactanalytics.co.

Some of our accolades include:

- Ranked as one of America's Fastest-Growing Companies by Financial Times for five consecutive years: 2020-2024.
- Ranked as one of America's Fastest-Growing Private Companies by Inc. 5000 for seven consecutive years: 2018-2024.
- Voted #1 by more than 300 retailers worldwide in the RIS Software LeaderBoard 2024 report.
- Ranked #72 in America's Most Innovative Companies list in 2023 by Fortune alongside companies like Microsoft, Tesla, Apple, IBM, etc.
- Forged a strategic partnership with Google to equip retailers with cutting-edge generative AI tools.
- Recognized in multiple Gartner reports, including Market Guides and Hype Cycle, spanning assortments, merchandising, forecasting, algorithmic retailing, and Unified Price, Promotion, and Markdown Optimization Applications.

About the Role

We are seeking a visionary AI Architect to lead the design and development of our next-generation Agentic AI Studioa platform focused on intelligent, autonomous GenAI agents capable of reasoning, decision-making, and multi-step task execution across enterprise workflows. This is a hands-on architecture leadership role, where you will work at the intersection of LLMs, cognitive agents, tool use, and multi-agent orchestration, collaborating with product, research, and engineering teams to build scalable systems that go beyond traditional prompt engineering.

Responsibilities

- 10+ years (including 4+ years in GenAI and autonomous agent systems)
- Proven experience designing LLM-powered agents with long-term memory, tool-use, planning, and goal-directed reasoning
- Strong expertise in agentic frameworks like AutoGen, CrewAI, LangGraph, ReAct, CAMEL and BabyAGI
- Experience in multi-agent orchestration, task delegation, and collaborative planning between agents
- Deep knowledge of RAG pipelines, vector search (e.g., FAISS, Weaviate, Pinecone), and embedding strategies
- Strong proficiency in LLM fine-tuning (SFT, LoRA, PEFT) and working with Hugging Face, OpenAI, Claude, etc.
- Skilled in building and integrating tool-augmented agents using APIs, retrieval plugins, code execution, etc.
- Familiarity with LangChain, LangGraph, LangFlow, and other orchestration libraries
- Deep hands-on with Python, FastAPI, cloud services (AWS, GCP, Azure), and infrastructure automation (Terraform, CI/CD)
- Exposure to cognitive architectures (SOAR, ACT-R, OpenCog, Leabra)
- Understanding of agent simulation environments, reinforcement learning, or curriculum-based training
- Experience integrating agents into LMS, enterprise SaaS, or knowledge work platforms
- Knowledge of event-driven, goal-based task scheduling and real-time coordination systems
- Familiarity with Langfuse, PromptLayer, Traceloop, or custom observability pipelines for agents
- Prior work on memory management, context windows, and scalable vector DB partitioning
- Defining the overall architecture of Agentic AI Studio, from agent lifecycle to orchestration and observability
- Designing and deploying scalable agent runtimes, leveraging cloud-native tools and GPU inference platforms
- Leading development of multi-agent coordination protocols, goal resolution, and autonomous workflows
- Driving experimentation with new agentic behaviors, planner-tool integration, and long-term memory systems
- Collaborating with data, infra, and product teams to integrate agentic intelligence into real-world business tasks
- Establishing architectural standards for observability, explainability, and safety of AI agents
- Mentoring engineers and evangelizing agent-based AI thinking across the organization

Qualifications

- Education details

Required Skills

- Strong expertise in agentic frameworks
- Proven experience designing LLM-powered agents
- Deep knowledge of RAG pipelines and vector search
- Strong proficiency in LLM fine-tuning
- Skilled in building and integrating tool-augmented agents
- Deep hands-on with Python and cloud services

Preferred Skills

- Familiarity with orchestration libraries
- Exposure to cognitive architectures
- Understanding of agent simulation environments