AI Architect

About the Company

TheMathCompany or MathCo® is a global Enterprise AI and Analytics company trusted by leading Fortune 500 and Global 2000 enterprises for data-driven decision making. Founded in 2016, MathCo builds custom AI and advanced analytics solutions to solve enterprise challenges through its hybrid model. NucliOS, MathCo’s proprietary platform, enables connected intelligence at a lower total cost of ownership (TCO).

At MathCo, we foster an open, transparent, and collaborative culture, making it a great place to work. We provide exciting growth opportunities and value capabilities and attitude over experience, enabling our Mathemagicians to 'Leave a Mark'.

We’re looking for a seasoned AI Architect with deep expertise in cloud-native enterprise systems and Generative AI. You will define and deliver scalable, secure, and production-grade GenAI architectures — including multi-agent, RAG, LLMOps and AgentOps systems — and lead cross-functional teams to build and operate them. This role combines hands-on technical leadership, systems thinking, and strong stakeholder management.

Responsibilities:

Architecture & System Design

• Design scalable, modular, and cloud-native architectures for GenAI applications (microservices, event-driven, serverless).
• Define system boundaries, data flows, orchestration, and integration patterns for LLMs, vector DBs, embedding services, and tool integrations.
• Produce architecture artifacts (Layered Architecture Diagrams, C4 Models, DFDs, Class, Sequence, ER & Use Case diagrams, different types of blueprints, API contracts, design and trade-off decisions).

GenAI & Agentic Systems

• Architect and deliver Retrieval-Augmented Generation (RAG) pipelines, Natural Language to SQL Flows, fine-tuning strategies, multi-modal capabilities, and tool-augmented agents.
• Design agent orchestration and multi-agent frameworks enabling planning, reasoning, and secure tool invocations, implement and design Agent prototypes and Communication Protocols.
• Define prompt engineering standards, memory models (episodic/semantic/procedural), and context management.
• Agentic AI security — provable agent identity/attestation, tool allowlist + human gate for high-risk actions; ephemeral scoped tokens, sandboxed execution, and replayable audit traces.

Observability, Ops & Cost Optimization

• Define telemetry, tracing, and logging for models and agents; monitor performance, drift, hallucination rates and user feedback loops.
• Build dashboards, alerts and runbook guidance for operational health.
• Design systems for cost efficiency (autoscaling, spot instances, serverless choices) and support FinOps practices.

Leadership, Collaboration & Documentation

• Lead cross-functional teams (product, data science, AI engineers, platform) through architecture reviews, workshops, and technical decisioning.
• Maintain architectural standards, documentation, playbooks, and pattern libraries for GenAI systems.
• Mentor engineers and evangelize best practices across the organization.

Qualifications

• 10+ years software engineering experience with 3+ years in architecture or senior technical leadership roles (or equivalent).
• Proven track record designing and delivering cloud-native, production systems at enterprise scale.
• Hands-on experience with GenAI/LLM systems, RAG, NL-SQL, agentic frameworks or similar productionized AI applications.
• Strong knowledge of system design patterns (microservices, event-driven, CQRS, hexagonal architecture), and Low Level Design Patterns.
• Experience integrating ML/LLM services with enterprise data platforms and APIs while meeting security/compliance requirements.
• Solid engineering background in at least two languages (Python, TypeScript, Go, Java, C#) and familiarity with modern frameworks.

Required Skills

Technical skills & technologies (comprehensive)

• Cloud & Infra: AWS / Azure / GCP; Kubernetes, Docker, serverless (Lambda, Functions, Cloud Run), GPU instances.
• GenAI & ML: Hugging Face Transformers, OpenAI APIs, LangChain, LlamaIndex, Semantic Kernel, Haystack.
• Vector Stores: FAISS, Pinecone, Weaviate, Chroma, Postgres+pgVector, and other cloud vector stores.
• LLMOps / MLOps: Custom Development of Ops Pipelines, MLflow, TFX, BentoML, Kubeflow.
• Data & Integration: Kafka, Spark, Airflow, Flink, ETL/ELT concepts, data lakes, API gateways (Apigee etc).
• DevOps & IaC: Terraform, Pulumi, CloudFormation, GitHub Actions, Jenkins.
• Observability & Security: Prometheus, Grafana stack, OpenTelemetry, Jaeger, ELK, Datadog; Vault, IAM, LDAP/OAuth2/OIDC/SAML Connect, Snyk, SonarQube, SAST/SCA in pipelines, OWASPs, CWEs, CVEs.
• Databases & Storage: Relational (RDS/Cloud SQL), NoSQL (Mongo, DynamoDB, Cosmos DB), Redis, S3/Blob/GCS, ORM/ODM frameworks.
• Agent frameworks / tools: Understanding of Basics of Agents required, Langgraph, Autogen, AutoGPT, AgentVerse, MetaGPT, CrewAI etc.
• Performance & scalability: SSR/ISR, caching strategies (CDN, edge), lazy loading, bundle optimization, performance budgets.
• Realtime & async: WebSockets, SSE, message brokers (Kafka, RabbitMQ), background workers.
• Frontend frameworks: React (Next.js), Angular, Vue; component libraries and state (Redux/RTK, Context, Pinia, Zustand).
• Styling & UI tooling: Component Libraries, Accessibility best practices, Responsive UI.
• Frontend build & tooling: Vite, Webpack, Storybook, UI Frameworks.
• Backend frameworks: Node.js/Express, FastAPI, serverless functions (AWS Lambda, Cloud Functions).
• API design & integration: REST, gRPC, OpenAPI/Swagger, API versioning and contract testing, GraphQL (Optional).
• UX & product mindset: Design-system familiarity, usability, accessibility