Senior AI Platform Engineer

Senior AI Platform Engineer

Position Type: Full-time (Onsite)

About Intelmatix

Intelmatix is a deep tech Artificial Intelligence (AI) company founded in July 2021 by a group of MIT scientists with the vision of transforming enterprises to become cognitive. A cognitive enterprise is one that uses AI and Decision Intelligence in making their decisions. This leads to better business decisions with improved accuracy, reduced errors, and better outcomes across various aspects of the business.

Job Summary

We are seeking a Senior AI Platform Engineer to join our Platform team. You will design and build the core platform infrastructure that powers EDIX, our enterprise Decision Intelligence product — spanning foundational platform services, API layers, and AI-powered capabilities including agentic frameworks, retrieval pipelines, and LLM orchestration. This is a hands-on engineering role where you own the systems that make EDIX work.

The ideal candidate is first and foremost a strong platform engineer — someone who builds scalable, well-architected systems — with deep experience applying that skill to AI-powered products. You understand the full stack from API gateways and service orchestration through to LLM integration and containerized deployment on AWS/GCP. Also the candidate must have a track record of building and operating production AI systems — not prototypes, not POCs — real systems serving real users under real constraints.

What You'll Be Doing

Platform Development

• Design, develop, and maintain the core platform services that power EDIX

• Implement foundational platform components: API gateways, service orchestration, configuration management

• Build shared infrastructure: dynamic configuration, service discovery, inter-service communication

AI Infrastructure & Stores

• Design abstraction layers (factory patterns) for vector stores, LLMs, and other AI components

• Support multiple backend implementations (OpenSearch, Pinecone, pgvector)

• Build and optimize retrieval pipelines across vector databases, graph databases, and embedding stores

• Implement agentic frameworks and orchestration patterns (LangGraph, LangChain, or similar)

• Support multiple LLM providers (AWS Bedrock, OpenAI, Anthropic) through unified interfaces

• Build data pipelines for document and media processing (ingestion, chunking, embedding, indexing)

Platform APIs & Services

• Develop and maintain highly-available API endpoints (RESTful, async patterns)

• Serve platform capabilities to both internal teams and client-facing products

• Implement error handling, retry logic, caching strategies, and fallback patterns

• Optimize latency and throughput for both traditional and AI inference workloads

• Design APIs with extensibility and multi-tenancy in mind

Quality & Standards

• Write clean, well-tested, documented code with comprehensive unit and integration tests

• Participate in code reviews and contribute to technical documentation and runbooks

• Establish and enforce engineering standards for the platform codebase

Collaboration

• Work with the AI Platform Lead on architecture decisions and technical direction

• Support the Delivery team with platform capabilities for client engagements

• Coordinate with DevOps on deployment, scaling, and infrastructure optimization

• Collaborate with Data Science and ML teams to productionize models as platform services

Qualifications

Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field, or equivalent professional experience.

Experience: Minimum of 6 years in software engineering, with at least 3 years experience in platform development and platform-wide solutions and At least 1 year working with AI/Gen-AI systems in production.

Technical Skills:

• Strong proficiency in Python; additional experience in Java, Go, or TypeScript is a plus

• Strong understanding of system design, platform architecture, distributed systems, and data modeling

• Strong API development skills (REST, async patterns, WebSockets) and experience designing platform APIs consumed by multiple teams

• Experience with cloud platforms and infrastructure — AWS preferred (Lambda, SQS, OpenSearch, RDS, Bedrock, S3)

• Experience with agentic frameworks (LangChain, LangGraph, CrewAI, or similar) and orchestration patterns

• Experience with containerization (Docker) and orchestration (Kubernetes, ECS) including deployment, scaling, and event-driven patterns

• Experience with Infrastructure-as-Code (IaC) tools like Terraform, CloudFormation.

• Experience with CI/CD pipelines (GitHub Actions, Jenkins, AWS CodePipeline)

• Experience with using LLM APIs and providers (AWS Bedrock, OpenAI, Anthropic, or similar)

• Experience with vector databases and embedding stores (OpenSearch, Pinecone, pgvector, or similar)

• Experience with monitoring and observability tools (Prometheus, Grafana, DataDog, CloudWatch) — including AI-specific metrics (latency, token usage, quality)

• Familiarity with prompt engineering, evaluation frameworks.

• Familiarity with MCP (Model Context Protocol), A2A (Agent to Agent Protocol) and tool-use patterns

Soft Skills:

• Excellent communication skills, adaptable, self-motivated, and capable of thriving in a fast-paced, unstructured startup environment

• Platform mindset — you think about reusability, extensibility, and how other teams will consume what you build

• Strong ownership mentality — you ship production systems, not experiments

Nice to Have

• Experience building scalable, multi-tenant platform systems consumed by multiple product teams

• Experience with RAG (Retrieval-Augmented Generation) architectures and optimization techniques

• Exposure to both NoSQL and SQL database technologies (DynamoDB, MongoDB, PostgreSQL, AWS Aurora, OpenSearch)

• Experience with API gateway and service mesh patterns (Kong, Envoy, AWS API Gateway)

• Familiarity with modern authentication schemes such as OAuth, JWTs, or cookies

• Exposure to graph databases (Neo4j, Amazon Neptune) and knowledge graph construction

• Experience working in fast-paced startup environments, contributing to rapid development and delivery-driven projects

• Proven ability to thrive and adapt during significant company growth