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Own the end-to-end backend architecture, scalability, and reliability of an AI platform transitioning to production. Lead the design of AI pipelines, distributed worker systems, and AWS deployment strategies while mentoring senior engineers.
Requires over 12 years of backend experience with expert-level Python and deep expertise in distributed systems and PostgreSQL. Must have proven experience with AWS production environments and integrating LLM APIs into scalable platforms.
Job Position: Principal Backend Engineer
Experience: 12+ Years
Work Mode: Remote (Full-Time) - Minimum 4-hour overlap with US time zones
ROLE OVERVIEW
We are seeking a Principal Backend Engineer to own the scalability, reliability, and architectural evolution of an AI platform as it transitions from a prototype to a production-grade platform. This is a high-impact, high-ownership role at the intersection of distributed systems engineering and applied AI.
KEY RESPONSIBILITIES
● Platform Architecture & Scalability: Own end-to-end backend architecture for the AI platform; design for multi-tenant, high-throughput extraction workloads
● Extraction Pipeline Engineering: Architect and evolve AI pipelines, define pipeline abstractions, registry patterns, and execution strategies that balance accuracy, latency, and LLM API cost.
● Multi-Provider LLM Abstraction: Enhance and harden the unified LLM provider layer ; implement multi-key round-robin pooling, structured output schemas, tool-calling protocols, and provider failover logic.
● Async Worker & Queue Architecture: Design the distributed worker system for heavy pipeline execution observable queue with dead-letter handling, retry policies, and backpressure management.
● AWS Production Deployment: Lead the AWS deployment architecture (ECS/EC2, RDS PostgreSQL, S3, Bedrock, ALB, Secrets Manager, CloudWatch); define IaC, blue-green deployment strategies, and ensure the platform meets security, compliance, and data residency requirements.
● API Design & Governance: Define and enforce API design standards (OpenAPI 3.1, spec-first, versioning, deprecation); own the 25+ FastAPI endpoints, request/response schema evolution, and backward compatibility guarantees.
● Data Architecture: Own the PostgreSQL schema for extraction metadata (holes, dimensions, GD&T, title blocks, notes, extraction traces, batches); design indexing strategies, multi-tenant isolation, and efficient querying patterns for the document review and dataset export workflows.
● Security & Compliance: Implement secure service-to-service communication, secrets management via AWS Secrets Manager, IAM role-based access for Bedrock and S3, and CORS/auth policies
● Observability & Reliability: Instrument the platform with structured logging, distributed tracing (AWS X-Ray / OpenTelemetry), and CloudWatch alarms; define SLOs for pipeline throughput, LLM call latency, and extraction accuracy.\
● Engineering Leadership: Mentor senior engineers, conduct architecture and design reviews, set coding standards, and drive the technical roadmap for the enterprise.
REQUIRED SKILLS & EXPERIENCE
● 12+ years of backend engineering experience with a strong focus on distributed systems and production-grade platform design. ● Expert-level Python proficiency; deep hands-on experience with FastAPI, SQLAlchemy, Pydantic v2, and async/concurrent programming (asyncio, ThreadPoolExecutor).
● Proven experience designing and operating microservices at scale — including service decomposition, inter-service communication, and failure isolation strategies.
● Strong PostgreSQL expertise: schema design, indexing, query optimization, multi-tenant isolation (RLS or schema-per-tenant), and migration management.
● Hands-on AWS production experience: EC2/ECS, RDS, S3, ALB, IAM, Secrets Manager, CloudWatch, and ideally Amazon Bedrock.
● Deep understanding of event-driven and async architectures: message queues, polling workers, idempotency guarantees, retry strategies, and backpressure handling.
● Experience integrating LLM APIs (any of: Anthropic Claude, OpenAI, Google Gemini, Mistral, AWS Bedrock) in production — including rate limiting, structured output enforcement, and multi-provider failover.
● Experience with container-based deployments using Docker and Docker Compose; familiarity with ECS task definitions and service orchestration.
● Strong API lifecycle management skills: OpenAPI 3.1, versioning, backward compatibility, deprecation policies, and governance frameworks. \
● Practical knowledge of distributed system patterns: sagas, circuit breakers, idempotency keys, at-least-once delivery, and graceful degradation.
● Security engineering fundamentals: OAuth2, JWT, RBAC, CORS, secrets management, and cloud IAM role design.
● Prior experience building developer platforms, internal tooling, or AI/ML serving platforms at large-scale organizations.
PREFERRED / GOOD TO HAVE
● Direct experience building AI/LLM extraction pipelines — structured output, tool-calling, multi-agent orchestration (LangGraph, CrewAI, or custom frameworks).
● Familiarity with agentic pipeline patterns: planner-executor swarms, multi-round tool-use loops, shared notepad patterns, and confidence-based validation.
● Experience with Amazon Bedrock cross-region inference and IAM-based model access (Amazon Nova, Claude via Bedrock).
● Knowledge of ML model serving infrastructure: HuggingFace Transformers, PyTorch model loading, YOLO/object detection integration, ChromaDB vector stores.
● Experience with PDF processing pipelines: rasterization (PyMuPDF), coordinate normalization, multi-page extraction, and bounding box annotation workflows.
● Familiarity with service mesh (Istio/Linkerd), Kubernetes, or EKS for cloud-native deployment evolution.
● Experience with workflow orchestration frameworks (Temporal, Airflow, or equivalent) for long-running, stateful extraction jobs.
● Experience in high-scale SaaS platforms with strict SLAs, multi-region deployments, and enterprise security requirements.
SUMMARY This role is for a Principal-level engineer who can own backend architecture at the intersection of large-scale distributed systems and applied AI. The ideal candidate has built production platforms that serve real enterprise workloads, has deep LLM integration experience, and can lead a team while staying hands-on in the most critical technical decisions.
Datamaxis is a WMBE corporation and committed to provide IT services to commercial and government organizations.
DATAMAXIS takes pride in delivering a wide range of business IT modernization, data analytics, and technology management services. With command of the cutting-edge developments in these fields, our team and consultants are ready to provide you a robust technology modernization experience that results in a big boost in performance capability and operational efficiency.
Offices: 1767 Business Center Dr, 200, Reston, Virginia 20190, US · 28345 Beck Rd, 304, Wixom, Michigan 48393, US
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