AI Solution Architect

AI Architect – Enterprise AI & Generative AI ( Remote) - Contractual

Experience: 8+ years

Location: Remote, India

Employment Type: Contractual

Working Hours: 3:00 PM – 12:00 AM IST

Client Engagement: US-based client

About the Role

We are looking for a hands-on, client-facing AI Architect to lead the architecture, technical direction, and production implementation of enterprise AI and Generative AI solutions for a US-based client.

You should combine strong software engineering and cloud architecture fundamentals with practical experience building secure, scalable, and reliable AI applications. This role involves end-to-end technical ownership, from solution design and prototyping to production deployment and optimization. It is not a research-only, advisory-only, or prompt-writing position.

Key Responsibilities

• Lead end-to-end architecture and delivery of AI/ML and Generative AI solutions, from discovery and proof of concept to production rollout.
• Design LLM-powered applications using Retrieval-Augmented Generation (RAG), embeddings, vector databases, AI agents, and orchestration frameworks.
• Define data ingestion, chunking, retrieval, grounding, evaluation, and knowledge-refresh strategies.
• Select appropriate AI models and platforms based on quality, latency, cost, security, and operational requirements.
• Design cloud-native architectures, APIs, integrations, identity controls, monitoring, and resilient deployment patterns.
• Create architecture diagrams, technical designs, implementation roadmaps, and technical standards.
• Implement AI quality and safety measures, including hallucination mitigation, guardrails, access controls, and prompt-injection defenses.
• Guide engineering, data, ML, and DevOps teams through implementation, code reviews, troubleshooting, and go-live readiness.
• Lead technical discussions and design reviews with US client stakeholders.
• Establish production monitoring, CI/CD, versioning, incident handling, and performance and cost optimization practices.

Required Skills & Qualifications

• 8+ years of relevant experience in software engineering, AI/ML engineering, data engineering, or technology architecture.
• Proven experience personally architecting and deploying at least one enterprise-grade AI or Generative AI solution into production.
• Strong practical knowledge of LLMs, RAG, embeddings, vector databases, orchestration frameworks, and model evaluation.
• Hands-on experience with Python and APIs, including prototyping, code review, and troubleshooting.
• Experience with at least one major cloud platform: Microsoft Azure, AWS, or Google Cloud Platform (GCP).
• Understanding of data pipelines, SQL/NoSQL databases, containers, CI/CD, MLOps, and LLMOps.
• Knowledge of enterprise security, privacy, identity and access management, and responsible AI practices.
• Experience independently leading architecture workshops, design reviews, and technical discussions with US clients.
• Excellent written and spoken English, documentation, and stakeholder management skills.
• Availability to work from 3:00 PM to 12:00 AM IST and willingness to accept a contractual engagement.

Preferred Experience

• Azure AI Foundry, Azure OpenAI, Amazon Bedrock, or Google Vertex AI.
• Azure AI Search or equivalent retrieval tools.
• LangChain, LangGraph, Semantic Kernel, and agent/tool orchestration.
• Model fine-tuning, predictive ML, document intelligence, multimodal AI, or real-time AI applications.
• Experience in cloud modernization, enterprise integrations, data analytics, or technology consulting.
• Relevant AI/cloud architecture certifications and experience mentoring technical teams.

What Success Looks Like

• Designing secure, scalable, production-ready AI solutions rather than demos or conceptual presentations.
• Making sound technical decisions around feasibility, performance, reliability, cost, and AI risks.
• Taking ownership of architecture decisions and resolving technical blockers with limited supervision.
• Building trust with senior US stakeholders and collaborating effectively with India-based delivery teams.

Contract Details

• Initial engagement: Minimum 3 months, with extension subject to project requirements and performance.
• Working schedule: 3:00 PM – 12:00 AM IST.
• Potential permanent employment: After 18 months, the client may consider direct permanent employment at its sole discretion. Conversion is not guaranteed.

Mandatory Screening Criteria

Candidates should be able to:

• Explain an enterprise AI/GenAI solution they personally architected and deployed, including architecture, scale, evaluation, and business impact.
• Whiteboard an enterprise RAG or agentic solution covering data flow, security, authentication, authorization, monitoring, and cost controls.
• Discuss real-world model-quality issues, hallucinations, privacy risks, incidents, and architectural trade-offs.
• Lead a technical design review with US client stakeholders.
• Confirm acceptance of the working hours and contractual engagement.

Apply only if you have hands-on experience architecting and deploying enterprise AI/GenAI solutions in production.

About the Role

Antier Solutions is looking for a technically strong AI Solution Architect to drive the architecture, design, and technical execution of AI-powered solutions.

The role requires a combination of deep technical expertise, architectural ownership, and the ability to guide engineering teams in building scalable, production-ready AI systems. The individual will evaluate business problems, define technical approaches, make architecture decisions, and work closely with developers and stakeholders to translate ideas into working solutions.

This is a hands-on architecture and technical leadership role, not one limited to documentation, project coordination, or pre-sales presentations.

Key Responsibilities

1. AI Architecture & Solution Design

• Design end-to-end architectures for AI, Generative AI, LLM-based applications, and agentic AI systems.
• Evaluate and select appropriate models, frameworks, orchestration approaches, and technology stacks.
• Design solutions involving RAG, AI agents, workflow automation, APIs, databases, and enterprise integrations.
• Define architecture standards covering scalability, security, performance, maintainability, and deployment.
• Evaluate technical feasibility, identify risks, and recommend practical implementation approaches.

2. Technical Leadership & Implementation

• Provide architectural direction and technical guidance to AI engineers and developers.
• Review proposed designs and implementation approaches, identifying technical gaps and potential bottlenecks.
• Establish reusable architecture patterns, development standards, and engineering best practices.
• Troubleshoot complex technical challenges and contribute hands-on where necessary.
• Guide teams in adopting AI-assisted development and agentic workflows where they improve engineering productivity.

3. Production Readiness & Quality

• Design solutions with appropriate authentication, access controls, logging, monitoring, testing, and evaluation mechanisms.
• Address AI-specific challenges such as hallucinations, retrieval quality, model selection, latency, reliability, and inference costs.
• Define appropriate human oversight and validation mechanisms for sensitive or consequential workflows.
• Guide deployment decisions across suitable cloud, on-premise, and hybrid environments.

4. Business Alignment & Delivery Ownership

• Understand business requirements and translate them into implementable technical solutions.
• Work with stakeholders to establish scope, dependencies, estimates, resource requirements, and delivery milestones.
• Collaborate with engineering and delivery teams to move solutions from proof of concept to production.
• Balance rapid execution with architectural quality, maintainability, and commercial feasibility.
• Communicate technical decisions, risks, and trade-offs clearly to business and technical stakeholders.

Required Skills & Experience

• B.Tech, BE, MCA, MSc, or equivalent technical qualification in Computer Science, IT, Data Science, or a related discipline.
• 8+ years of overall experience in software engineering, AI engineering, solution architecture, or related technical roles.
• Strong hands-on experience building AI/ML or Generative AI applications, with demonstrated responsibility for architecture and implementation decisions.
• Strong understanding of LLMs, RAG pipelines, agentic AI systems, tool calling, orchestration, and AI-driven automation.
• Experience with Python and relevant AI frameworks, libraries, and integration technologies.
• Strong understanding of APIs, databases, data pipelines, distributed systems, and application architecture.
• Experience evaluating AI solutions for accuracy, reliability, performance, security, and cost.
• Ability to guide developers, review technical approaches, resolve complex problems, and establish reusable engineering practices.
• Experience deploying or supporting AI applications in production environments.
• Strong analytical, communication, and technical decision-making skills.

Preferred Qualifications

• Experience architecting multi-agent systems or complex AI automation workflows.
• Exposure to cloud platforms, containerization, CI/CD, observability, and scalable AI infrastructure.
• Experience mentoring engineering teams and introducing new AI development practices.
• Experience with enterprise AI deployments, international clients, or technically complex business environments.

Ideal Candidate Profile

• We are looking for an individual who can independently own technical architecture, guide engineering teams, and make sound implementation decisions. The candidate should combine hands-on AI expertise with the ability to turn business requirements into scalable, production-ready solutions.
• Candidates whose experience is limited to AI research, business analysis, project coordination, or pre-sales documentation without meaningful technical implementation and architecture ownership may not be suitable.

Looking for an Architect with strong experience in AI and Data Engineering, preferably with hands-on experience in Generative AI (GenAI). Candidates with experience in any cloud platform (AWS, Azure, or GCP) will be considered, 

Job Details

Position Title: AI Evangelist

Role Purpose

We are seeking a highly experienced AI Evangelist & Core AI Engineer who combines deep technical expertise in Artificial Intelligence with the ability to influence, educate, demonstrate, and accelerate AI adoption across the enterprise.

The role requires deep understanding of AI, Machine Learning, Deep Learning, Generative AI, Large Language Models, Agentic AI, AI engineering, and modern software development practices, combined with significant hands-on experience implementing scalable AI applications in complex enterprise environments.

Key Responsibilities

1. Core AI Engineering & Architecture

• Design, develop, and implement enterprise-grade AI and Generative AI applications.
• Build scalable applications using LLMs, multimodal models, AI agents, RAG, vector databases, knowledge graphs, APIs, and enterprise data platforms.
• Architect AI solutions capable of supporting enterprise-scale workloads with appropriate considerations for:
• Scalability
• Reliability
• Security
• Performance
• Observability
• Cost optimization
• Governance
• Develop and implement advanced Retrieval-Augmented Generation (RAG) architectures including hybrid search, reranking, metadata filtering, semantic retrieval, and knowledge grounding.
• Build multi-agent and agentic AI applications capable of reasoning, tool usage, workflow execution, orchestration, and enterprise system integration.
• Design AI applications integrating structured and unstructured enterprise information.
• Evaluate architecture choices between commercial models, open-source models, small language models, domain-specific models, and hosted AI platforms.
• Implement model routing, prompt orchestration, caching, guardrails, evaluation pipelines, and fallback strategies.
• Work closely with cloud, platform, security, enterprise architecture, DevOps, and application engineering teams.

2. Deep AI / ML / DL Expertise

The candidate should have strong conceptual and practical understanding across the modern AI technology landscape, including:

• Artificial Intelligence /Machine Learning/ Deep Learning/ Neural Networks /LLM/SLM/RAG/Graph RAG / MLOps / LLMOps / GenAIOps

3. Generative AI & LLM Engineering

• Build production-grade applications utilizing leading commercial and open-source LLM ecosystems.
• Design scalable LLM platforms supporting multiple enterprise applications and use cases.
• Develop reusable LLM services, APIs, components, agent frameworks, and reference architectures.

4. AI Agents & Automation

• Design and implement AI agents and agentic workflows that automate complex business and engineering processes.
• Build agents capable of interacting with enterprise applications, APIs, databases, documents, development environments, and workflow platforms.
• Evaluate emerging agent architectures and orchestration frameworks pragmatically.
• Develop reusable patterns for:
• Single-agent systems
• Multi-agent systems
• Human-in-the-loop workflows
• Autonomous workflows
• Tool-calling agents
• Coding agents
• Enterprise knowledge agents
• Establish appropriate controls for AI agent identity, permissions, auditability, security, and human oversight.

5. AI-Enabled Software Engineering

Drive adoption of AI throughout the Software Development Life Cycle.

Job Title: Senior AI Engineer

Experience: 5–10 years in software engineering, with 3+ years in AI/ML engineering preferred

Employment Type: Full-time

Department: Artificial Intelligence / Engineering

Location: Bangalore

We are looking for a highly skilled Senior AI Engineer to design, develop, deploy, and scale intelligent AI-powered applications and solutions. The ideal candidate will have strong expertise in machine learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and production-grade software engineering.

The candidate will work closely with product managers, data scientists, software engineers, and business stakeholders to translate complex business requirements into scalable, reliable, secure, and cost-effective AI solutions.

Key Responsibilities

1. AI Solution Design and Development

• Design, develop, and deploy end-to-end AI and machine learning solutions.
• Build intelligent applications using LLMs, Generative AI, NLP, and deep learning techniques.
• Develop scalable AI architectures and reusable components for enterprise applications.
• Evaluate AI models and select appropriate approaches based on accuracy, latency, cost, and business requirements.

2. Generative AI and LLM Engineering

• Develop LLM-powered applications using models from providers such as OpenAI, Anthropic, Google, or open-source ecosystems.
• Design and implement RAG pipelines, document processing workflows, semantic search, and knowledge retrieval systems.
• Build AI agents and multi-step workflows using appropriate orchestration frameworks.
• Implement prompt engineering, structured outputs, tool calling, and context management.
• Optimize model performance through evaluation, fine-tuning where appropriate, and inference optimization.

3. Machine Learning and Data Engineering

• Develop and operationalize machine learning models for classification, prediction, recommendation, and other business use cases.
• Work with structured, unstructured, and multimodal data.
• Build data preprocessing, feature engineering, model training, and validation pipelines.
• Apply appropriate model evaluation techniques and statistical methods.

4. Production Deployment and MLOps / LLMOps

• Deploy AI solutions using cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Build automated CI/CD pipelines for AI applications and model deployment.
• Implement model versioning, experiment tracking, monitoring, and performance evaluation.
• Optimize inference latency, throughput, infrastructure utilization, and operating costs.
• Establish observability for AI applications, including token usage, retrieval quality, latency, failures, and model behavior.

5. Software Engineering and System Architecture

• Develop production-quality applications and APIs using Python and modern backend frameworks.
• Design RESTful APIs, asynchronous processing systems, and microservices where appropriate.
• Integrate AI services with enterprise applications, databases, and third-party systems.
• Use relational databases, vector databases, caching, and distributed systems effectively.
• Apply software engineering best practices, including testing, code reviews, documentation, and design patterns.

6. Security, Quality, and Responsible AI

• Implement safeguards against prompt injection, data leakage, insecure tool execution, and unauthorized access.
• Apply appropriate privacy, security, and compliance controls to AI systems.
• Evaluate hallucinations, bias, reliability, and failure modes.
• Design human-in-the-loop workflows and fallback mechanisms for critical use cases.

7. Technical Leadership and Collaboration

• Lead technical design discussions and mentor junior AI engineers.
• Define engineering standards, coding practices, and AI development guidelines.
• Collaborate with cross-functional teams to deliver measurable business outcomes.
• Conduct code reviews, evaluate emerging technologies, and recommend practical innovations.
• Communicate technical trade-offs and solution architecture to both technical and non-technical stakeholders.

Required Technical Skills

Programming and Backend Development

• Strong proficiency in Python.
• Experience with FastAPI, Flask, or Django.
• Knowledge of REST APIs, asynchronous programming, data structures, algorithms, and software design principles.
• Experience with Git, automated testing, and CI/CD.

AI and Machine Learning

• Strong understanding of supervised and unsupervised learning, model evaluation, and statistical concepts.
• Hands-on experience with PyTorch, TensorFlow, or scikit-learn.
• Knowledge of NLP, embeddings, transformers, and deep learning fundamentals.

Generative AI and LLMs

• Hands-on experience building production applications using LLM APIs or open-source models.
• Strong understanding of RAG architecture, embeddings, chunking, reranking, and retrieval evaluation.
• Experience with vector databases such as Pinecone, Weaviate, Qdrant, Milvus, or pgvector.
• Familiarity with orchestration frameworks such as LangChain, LangChain, or comparable tools.
• Experience with tool calling, structured generation, agent workflows, and LLM evaluation.

Cloud and Infrastructure

• Experience with AWS, Azure, or Google Cloud.
• Working knowledge of Docker and Kubernetes.
• Familiarity with model serving, scalable APIs, monitoring, and production deployments.
• Experience with MLOps or LLMOps practices.

Databases and Data Processing

• Strong SQL skills and experience with relational databases.
• Familiarity with NoSQL databases, data pipelines, and distributed processing.
• Experience working with unstructured data, document ingestion, and retrieval systems.

Preferred Qualifications

• Experience with fine-tuning, quantization, or serving open-source LLMs.
• Experience with multimodal AI, including text, image, audio, or video.
• Knowledge of agent evaluation, workflow orchestration, and multi-agent systems.
• Experience with AI security, guardrails, governance, and responsible AI.
• Experience optimizing AI workloads for latency, scalability, and cost.
• Contributions to open-source AI projects, research, or technical publications.

Education

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• Equivalent practical experience and demonstrated engineering expertise may be considered.

Key Success Metrics

• Quality, accuracy, and reliability of deployed AI solutions.
• Production adoption and measurable business impact.
• Reduction in inference costs and response latency.
• RAG retrieval quality and reduction in unsupported or incorrect responses.
• System availability, scalability, and operational stability.
• Delivery timelines, code quality, and technical mentoring effectiveness.

Role Overview

The Principal Architect leads Byteridge’s Technology Strategy & Solutions Group (TSS). This is a senior, visible role responsible for defining technology point-of-view, shaping solution narratives, guiding enterprise conversations, and influencing revenue through differentiated thinking.

The Architect owns thought leadership, reference architectures, solution accelerators, and selective engagement on high-impact deals.

Key Responsibilities

• Own, enhance & execute Byteridge’s technology strategy across priority areas (Cloud, Data, Gen AI, Modernization).
• Create and maintain enterprise-grade reference architectures, solution blueprints, PoCs and accelerators.
• Lead strategic discovery workshops and executive-level solutioning for priority opportunities.
• Partner with Content Marketing to translate technical POVs into blogs, whitepapers, decks, webinars, and sales narratives.
• Enable the Enterprise Account Executive with differentiated solution stories and technical credibility.
• Build strong partnerships with Byteridge delivery teams to identify high-impact solutions and projects that can be leveraged as compelling capability showcases for existing customers and prospective clients.
• Work with Delivery Team Architects to influence delivery standards and architectural consistency across teams.
• Research market and industry trends across technologies, popular enterprise solutions, and buyer adoption patterns. Go deep into selected domains and verticals to continuously refine Byteridge’s technology strategy, solution approaches, and positioning.
• Act as a visible external voice through talks, webinars, and published content.

Ideal Profile

• 13–20 years of experience across technology architecture, solutioning, or technology consulting roles.
• Demonstrated ability to research and synthesize market trends, emerging technologies, and popular enterprise solutions.
• Experience developing deep expertise in specific domains or industry verticals and translating that into solution strategies.
• Strong background in modern software engineering, cloud platforms, data, AI, and enterprise systems.
• Proven track record of influencing client decisions and shaping solution direction, not just designing systems.
• Comfortable working at the intersection of technology, business strategy, marketing, and sales.
• Excellent communication skills with executive presence and the ability to articulate complex ideas clearly.

Success Metrics (KPIs)

• Quarterly technology and market POVs produced and adopted internally or externally.
• Creation and reuse of reference architectures, solution frameworks, and accelerators across deals.
• Number of high-impact delivery projects converted into capability showcases and sales assets.
• Influence on strategic opportunities, measured through deal quality, size, and AE feedback.
• Thought leadership visibility through blogs, webinars, talks, or industry participation.
• Internal adoption of architectural standards and solution approaches by delivery teams.

Technical Architect – Product Engineering

Experience: 15+ Years

Location: Pune, India

Employment Type: Full-time

Desired Skills: Python, Technical Architecture, AWS, Microservices, SaaS / Multi-tenant Architecture, Kubernetes, System Design

About the Role

We are looking for a Senior Technical Architect to lead the architecture, design, and technical evolution of an enterprise SaaS product. This is a hands-on leadership role requiring deep technical expertise, strong product engineering experience, and the ability to build scalable, secure, and high-performance platforms.

The ideal candidate should be passionate about solving complex engineering problems, driving innovation, mentoring development teams, and effectively leveraging AI to accelerate software development.

Key Responsibilities

• Own the overall product architecture and technical roadmap.
• Design and build scalable, secure, and highly available enterprise applications.
• Lead the design and implementation of new product features from concept to production.
• Remain hands-on with coding and contribute to critical product components.
• Drive architecture reviews, code quality, performance optimization, and engineering best practices.
• Lead cloud architecture, security, scalability, and DevOps initiatives.
• Evaluate and adopt modern technologies to improve product capabilities and engineering efficiency.
• Leverage AI tools (ChatGPT, GitHub Copilot, Cursor, Claude, etc.) to accelerate software development, code reviews, testing, documentation, debugging, and productivity.

Required Skills & Qualifications

• 15+ years of software product engineering experience with at least 5 years in a Technical Architect role.
• Strong hands-on expertise in Python and modern backend frameworks.
• Deep experience with AWS services and cloud-native application architecture.
• Strong understanding of DevOps, CI/CD pipelines, Infrastructure as Code (Terraform/CloudFormation), Docker, Kubernetes, and container orchestration.
• Experience designing microservices, REST APIs, event-driven architectures, and distributed systems.
• Strong knowledge of SQL and NoSQL databases.
• Experience with scalable SaaS platforms, multi-tenant architectures, and secure application design.
• Excellent understanding of software design patterns, performance tuning, observability, and system reliability.
• Strong analytical, problem-solving, and decision-making skills.

Strong Lead AI Engineer Profile with agentic systems architecture expertise

2

Mandatory (Experience 1): Must have 6+ years of software or ML engineering, with at least recent 2+ years building and shipping production LLM or agentic AI systems

3

Mandatory (Tech skill 1): Must have hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI), with experience debugging framework internals

4

Mandatory (Tech skill 2): Must have direct SDK experience with Anthropic Claude API (tool use, streaming), OpenAI Assistants API, or Vertex AI Agent Builder

5

Mandatory (Tech skill 3): Must have Python mastery — production-quality code, type annotations, unit and integration tests, packaging, and performance profiling

6

Mandatory (Tech skill 4): Must have RAG pipeline depth — embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS or custom evaluation harnesses

7

Mandatory (Tech skill 5): Must have cloud deployment experience with AWS, Azure, or GCP; Docker, Kubernetes, IaC basics (Terraform or CDK), and CI/CD pipelines

8

Mandatory (Tech skill 6): Must have agent observability experience — LangSmith, Helicone, or equivalent — with ability to diagnose latency, cost, and quality issues in production traces

9

Mandatory (Tech skill 8) : Must have owned full sub-system designs — agent topology, data flows, API contracts, failure handling — for a bounded scope, and built multi-agent graphs, tool orchestration, retrieval, and memory systems.

10

Mandatory (Tech skill 7): Must have track record of shipping 2+ agentic or ML systems to production — not just proof-of-concepts — with documented performance benchmarks

11

Mandatory (Communication): Must be able to write crisp component specifications and communicate architectural trade-offs to both engineers and non-technical stakeholders

Strong Lead AI Engineer Profile with agentic systems architecture expertise

2

Mandatory (Experience 1): Must have 6+ years of software or ML engineering, with at least recent 2+ years building and shipping production LLM or agentic AI systems

3

Mandatory (Tech skill 1): Must have hands-on proficiency with at least two agentic frameworks (LangGraph, LangChain, AutoGen, CrewAI), with experience debugging framework internals

4

Mandatory (Tech skill 2): Must have direct SDK experience with Anthropic Claude API (tool use, streaming), OpenAI Assistants API, or Vertex AI Agent Builder

5

Mandatory (Tech skill 3): Must have Python mastery — production-quality code, type annotations, unit and integration tests, packaging, and performance profiling

6

Mandatory (Tech skill 4): Must have RAG pipeline depth — embedding model selection, vector stores (Pinecone, Weaviate, pgvector), hybrid retrieval, RAGAS or custom evaluation harnesses

7

Mandatory (Tech skill 5): Must have cloud deployment experience with AWS, Azure, or GCP; Docker, Kubernetes, IaC basics (Terraform or CDK), and CI/CD pipelines

8

Mandatory (Tech skill 6): Must have agent observability experience — LangSmith, Helicone, or equivalent — with ability to diagnose latency, cost, and quality issues in production traces

9

Mandatory (Tech skill 8) : Must have owned full sub-system designs — agent topology, data flows, API contracts, failure handling — for a bounded scope, and built multi-agent graphs, tool orchestration, retrieval, and memory systems.

10

Mandatory (Tech skill 7): Must have track record of shipping 2+ agentic or ML systems to production — not just proof-of-concepts — with documented performance benchmarks

11

Mandatory (Communication): Must be able to write crisp component specifications and communicate architectural trade-offs to both engineers and non-technical stakeholders

12

Mandatory (Note 1) : Role is Hybrid, WFH flexibility as well upto 6 days a month

About the Role

We are a 25-year-old company building toward a technology-centered workforce where AI agents and automation handle more of the day-to-day work, supported by people with specialized expertise.

We are automating our existing workflows, expanding our use of agentic AI, launching a sister company next year, and building a fleet of Hermes AI agents to delegate and manage work.

We are looking for a hands-on AI Systems Architect & Company Technology Lead to help build, connect, and continuously improve these systems.

Key Responsibilities

·       Own and improve our Microsoft 365 technology environment and AI-powered workflows.

·       Build automations connecting email, meetings, Planner, and other business systems.

·       Research, test, implement, and maintain AI tools and integrations.

·       Build and manage the Hermes agent fleet, including shared memory, delegation rules, workflows, and reporting.

·       Connect AI agents to business systems and SaaS platforms using APIs, webhooks, databases, and automation tools.

·       Help build an internal agentic marketing department by selecting and connecting the right AI tools.

·       Continuously troubleshoot, improve, and document our technology systems.

·       Help apply these systems as the company expands into new ventures.

Must-Haves

·       Strong technical problem-solving and self-teaching ability.

·       Solid hands-on experience building, configuring, and managing AI agents.

·       Experience connecting AI agents and multiple SaaS platforms using APIs, webhooks, databases, and automation tools.

·       Comfortable with Python, JavaScript, or similar.

·       Ability to independently research, build, test, troubleshoot, and improve systems.

·       Strong interest in AI, automation, and emerging technology.

This is a rapidly changing field therefore Direct experience working with actual Hermes agents is not required. However, hands-on experience working with AI agents is required.

What we need is someone who already understands the fundamentals of deploying AI agents, giving them instructions and tools, connecting them to other systems, and making them work reliably in a real business environment.

The Opportunity

This is an opportunity to help us discover and implement what is actually possible with AI today, and to lead the process of expanding workflows along with the rapidly advancing capabilities — this role is not only to implement a technology that has already been figured out.