Job Description
Job Description
2025 marks the inflection point where AI evolved from productivity tool to autonomous teammate. Myridius is building AI-driven digital labor capabilities (AI-DLC) through our EVOQ platform—transforming how enterprises work. We need a visionary technical leader who can architect agentic AI solutions that don't just assist, but autonomously plan, execute, and deliver business outcomes.
You'll lead from the front: coding, consulting, and mentoring while driving our evolution from traditional IT services to AI-native solution delivery.
What You'll Do
1. Architect the Future: Agentic AI Led Engineering Innovation (40%)
Hands-On Engineering Leadership
- Lead by example: Code, prototype, and architect using Python, Lang Chain, Llama Index, and agentic frameworks
- Build autonomous agent systems using Ask-Plan-Agent patterns, multi-agent orchestration, and Model Context Protocol (MCP)
- Develop Myridius EVOQ's AI-DLC capabilities: agents that independently break down projects, plan execution, self-correct, and deliver outcomes
Platform Development
- Architect reusable AI frameworks and internal platforms that accelerate multi-client delivery
- Stay ahead of the curve: Research and implement emerging capabilities (multi-agent systems, small language models, multimodal AI)
- Drive market-differentiating innovation—solve unsolved industry problems, not just integrate APIs
Intellectual Property Creation
- Identify and develop proprietary algorithms and architectural patterns within EVOQ
- Lead patent filing efforts to establish defensible competitive advantages
- Build IP moat that positions Myridius uniquely in the agentic AI space
2. Transform Client Businesses: Strategic Consulting (35%)
C-Level Advisory
- Act as primary technical advisor to enterprise client executives
- Translate complex business challenges into high-ROI AI solutions with clear transformation roadmaps
- Guide clients through autonomous AI adoption—from pilot to production scale
Pre-Sales Excellence
- Partner with sales teams to architect winning proposals and compelling Po Cs
- Demonstrate thought leadership through client-facing technical presentations
- Define technical scope for multi-million dollar engagements
Production-Grade Delivery
- Ensure all solutions meet enterprise standards: reliability, security, scalability, and governance
- Architect MLOps pipelines and responsible AI frameworks that move beyond "notebook code"
3. Build World-Class Teams: Leadership & Mentorship (25%)
Talent Development
- Build and mentor high-performing AI Engineering and Data Science teams
- Foster culture of technical excellence, continuous learning, and innovation
- Develop next-generation leaders who can architect autonomous AI systems
Quality & Standards
- Establish best practices for agentic AI development, MLOps, CI/CD for AI
- Conduct strategic code reviews focused on architecture, scalability, and maintainability
- Implement responsible AI governance and ethical AI frameworks
What You Bring-
Core Technical Expertise- Enterprise & Cloud Architecture (Must-Have)
- Enterprise Architecture: Deep understanding of microservices, service mesh, API gateway patterns, event-driven architectures
- Full-Stack Development: Proficiency across frontend (React/Angular/Vue), backend (Python/Node.js/Java), and database layers
- Dev Ops & Infrastructure: Hands-on experience with CI/CD pipelines (Jenkins, Git Lab CI, Git Hub Actions), infrastructure as code (Terraform, Cloud Formation)
- Cloud Platforms: Production deployment experience on AWS, Azure, or GCP with deep knowledge of:
- Compute: EC2, Lambda, App Service, Cloud Functions
- Storage: S3, Blob Storage, Cloud Storage
- Networking: VPC, load balancers, CDN
- Security: IAM, Key Vault, Secret Manager, security best practices
Agentic AI & Autonomous Systems (Must-Have)
- Deep understanding of autonomous agent architectures (Ask-Plan-Agent, Re Act, multi-agent systems)
- Experience with orchestration frameworks: Lang Chain, Llama Index, DSPy, Auto Gen
- Hands-on with multi-agent orchestration platforms: n8n, Make (Integromat), Apache Airflow, or Prefect
- Familiarity with Model Context Protocol (MCP), agent memory systems, and tool integration
Generative AI Mastery
- Production experience with LLMs: Open AI GPT-4, Anthropic Claude, Llama 3, Mistral
- Advanced prompt engineering, Retrieval-Augmented Generation (RAG) architectures
- Fine-tuning expertise: PEFT, Lo RA, instruction tuning
ML/AI Foundation
- Expert-level Python, Py Torch/Tensor Flow, Scikit-learn
- Strong understanding of transformer architectures, embeddings, and vector operations
Data & Infrastructure
- Hands-on with Vector Databases: Pinecone, Milvus, Weaviate, Qdrant
- SQL/No SQL database design for AI applications (Postgre SQL, Mongo DB, Redis)
- Cloud AI platforms: AWS (Bedrock/Sage Maker), Azure (Open AI Service), GCP (Vertex AI)
MLOps & Production AI
- Production ML pipelines: MLflow, Kubeflow, or equivalent
- Containerization: Docker, Kubernetes for AI workloads
- CI/CD for machine learning systems
- Monitoring & observability: Prometheus, Grafana, Cloud Watch
Experience Requirements
- 18-22 years in software engineering, architecture, or consulting
- Minimum 2+ years of dedicated, hands-on AI/ML work (not just managing AI teams)
- Proven track record deploying AI solutions to production at enterprise scale
- Experience with autonomous agent systems or multi-step AI workflows (strong plus)
Strategic Impact
- Led technical teams (10+ engineers) in complex, multi-year programs
- Consulted with C-level stakeholders on technology transformation
- Contributed to patents, published research, or notable open-source projects (preferred)
Communication Excellence
- Exceptional ability to explain complex AI concepts to business leaders
- Comfortable presenting to boards and executive committees
- Can translate technical capabilities into business value narratives
Education
- Bachelor's or Master's degree in Computer Science, AI, Mathematics, or related field
- Ph D in AI/ML (plus, but not required)
Why Should You Apply?
- Ride the inflection point: Join at Q4 2025's agentic AI breakthrough—the once-in-a-decade moment when AI transforms from tool to autonomous teammate. Shape the market while it's being defined.
- Build something meaningful: Own EVOQ platform's technical evolution and create defensible IP through patents. This isn't maintenance—it's greenfield innovation with enterprise resources.
- Strategic influence at scale: Direct board-level exposure through reporting to Head of Solutions & Strategy. Guide multi-million dollar client transformations, not just implement requirements.
- Technical freedom + business impact: Lead from the front with hands-on coding while architecting solutions that redefine IT services delivery. No pure management trap—you'll build, mentor, and shape strategy.
Cultural Fit
You thrive in environments that value:
- Forward-thinking with pragmatic execution—ahead of market but with clear path to value
- Straight-shooting communication—no corporate speak, direct and actionable
- Continuous learning—AI is evolving weekly; curiosity is non-negotiable
- Outcome obsession—technology for business impact, not technology for its own sake
2025 marks the inflection point where AI evolved from productivity tool to autonomous teammate. Myridius is building AI-driven digital labor capabilities (AI-DLC) through our EVOQ platform—transforming how enterprises work. We need a visionary technical leader who can architect agentic AI solutions that don't just assist, but autonomously plan, execute, and deliver business outcomes.
You'll lead from the front: coding, consulting, and mentoring while driving our evolution from traditional IT services to AI-native solution delivery.
What You'll Do
1. Architect the Future: Agentic AI Led Engineering Innovation (40%)
Hands-On Engineering Leadership
- Lead by example: Code, prototype, and architect using Python, Lang Chain, Llama Index, and agentic frameworks
- Build autonomous agent systems using Ask-Plan-Agent patterns, multi-agent orchestration, and Model Context Protocol (MCP)
- Develop Myridius EVOQ's AI-DLC capabilities: agents that independently break down projects, plan execution, self-correct, and deliver outcomes
Platform Development
- Architect reusable AI frameworks and internal platforms that accelerate multi-client delivery
- Stay ahead of the curve: Research and implement emerging capabilities (multi-agent systems, small language models, multimodal AI)
- Drive market-differentiating innovation—solve unsolved industry problems, not just integrate APIs
Intellectual Property Creation
- Identify and develop proprietary algorithms and architectural patterns within EVOQ
- Lead patent filing efforts to establish defensible competitive advantages
- Build IP moat that positions Myridius uniquely in the agentic AI space
2. Transform Client Businesses: Strategic Consulting (35%)
C-Level Advisory
- Act as primary technical advisor to enterprise client executives
- Translate complex business challenges into high-ROI AI solutions with clear transformation roadmaps
- Guide clients through autonomous AI adoption—from pilot to production scale
Pre-Sales Excellence
- Partner with sales teams to architect winning proposals and compelling Po Cs
- Demonstrate thought leadership through client-facing technical presentations
- Define technical scope for multi-million dollar engagements
Production-Grade Delivery
- Ensure all solutions meet enterprise standards: reliability, security, scalability, and governance
- Architect MLOps pipelines and responsible AI frameworks that move beyond "notebook code"
3. Build World-Class Teams: Leadership & Mentorship (25%)
Talent Development
- Build and mentor high-performing AI Engineering and Data Science teams
- Foster culture of technical excellence, continuous learning, and innovation
- Develop next-generation leaders who can architect autonomous AI systems
Quality & Standards
- Establish best practices for agentic AI development, MLOps, CI/CD for AI
- Conduct strategic code reviews focused on architecture, scalability, and maintainability
- Implement responsible AI governance and ethical AI frameworks
What You Bring-
Core Technical Expertise- Enterprise & Cloud Architecture (Must-Have)
- Enterprise Architecture: Deep understanding of microservices, service mesh, API gateway patterns, event-driven architectures
- Full-Stack Development: Proficiency across frontend (React/Angular/Vue), backend (Python/Node.js/Java), and database layers
- Dev Ops & Infrastructure: Hands-on experience with CI/CD pipelines (Jenkins, Git Lab CI, Git Hub Actions), infrastructure as code (Terraform, Cloud Formation)
- Cloud Platforms: Production deployment experience on AWS, Azure, or GCP with deep knowledge of:
- Compute: EC2, Lambda, App Service, Cloud Functions
- Storage: S3, Blob Storage, Cloud Storage
- Networking: VPC, load balancers, CDN
- Security: IAM, Key Vault, Secret Manager, security best practices
Agentic AI & Autonomous Systems (Must-Have)
- Deep understanding of autonomous agent architectures (Ask-Plan-Agent, Re Act, multi-agent systems)
- Experience with orchestration frameworks: Lang Chain, Llama Index, DSPy, Auto Gen
- Hands-on with multi-agent orchestration platforms: n8n, Make (Integromat), Apache Airflow, or Prefect
- Familiarity with Model Context Protocol (MCP), agent memory systems, and tool integration
Generative AI Mastery
- Production experience with LLMs: Open AI GPT-4, Anthropic Claude, Llama 3, Mistral
- Advanced prompt engineering, Retrieval-Augmented Generation (RAG) architectures
- Fine-tuning expertise: PEFT, Lo RA, instruction tuning
ML/AI Foundation
- Expert-level Python, Py Torch/Tensor Flow, Scikit-learn
- Strong understanding of transformer architectures, embeddings, and vector operations
Data & Infrastructure
- Hands-on with Vector Databases: Pinecone, Milvus, Weaviate, Qdrant
- SQL/No SQL database design for AI applications (Postgre SQL, Mongo DB, Redis)
- Cloud AI platforms: AWS (Bedrock/Sage Maker), Azure (Open AI Service), GCP (Vertex AI)
MLOps & Production AI
- Production ML pipelines: MLflow, Kubeflow, or equivalent
- Containerization: Docker, Kubernetes for AI workloads
- CI/CD for machine learning systems
- Monitoring & observability: Prometheus, Grafana, Cloud Watch
Experience Requirements
- 18-22 years in software engineering, architecture, or consulting
- Minimum 2+ years of dedicated, hands-on AI/ML work (not just managing AI teams)
- Proven track record deploying AI solutions to production at enterprise scale
- Experience with autonomous agent systems or multi-step AI workflows (strong plus)
Strategic Impact
- Led technical teams (10+ engineers) in complex, multi-year programs
- Consulted with C-level stakeholders on technology transformation
- Contributed to patents, published research, or notable open-source projects (preferred)
Communication Excellence
- Exceptional ability to explain complex AI concepts to business leaders
- Comfortable presenting to boards and executive committees
- Can translate technical capabilities into business value narratives
Education
- Bachelor's or Master's degree in Computer Science, AI, Mathematics, or related field
- Ph D in AI/ML (plus, but not required)
Why Should You Apply?
- Ride the inflection point: Join at Q4 2025's agentic AI breakthrough—the once-in-a-decade moment when AI transforms from tool to autonomous teammate. Shape the market while it's being defined.
- Build something meaningful: Own EVOQ platform's technical evolution and create defensible IP through patents. This isn't maintenance—it's greenfield innovation with enterprise resources.
- Strategic influence at scale: Direct board-level exposure through reporting to Head of Solutions & Strategy. Guide multi-million dollar client transformations, not just implement requirements.
- Technical freedom + business impact: Lead from the front with hands-on coding while architecting solutions that redefine IT services delivery. No pure management trap—you'll build, mentor, and shape strategy.
Cultural Fit
You thrive in environments that value:
- Forward-thinking with pragmatic execution—ahead of market but with clear path to value
- Straight-shooting communication—no corporate speak, direct and actionable
- Continuous learning—AI is evolving weekly; curiosity is non-negotiable
- Outcome obsession—technology for business impact, not technology for its own sake
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