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Principal AI Engineer — Agentic Systems & AI Infrastructure
Hashmark Labs
3 hours ago
Full-time
On-site
Myrtle Point, Oregon, United States
Principal AI Engineer — Agentic Systems & AI Infrastructure
₹40,000 – ₹1.3L No equity
Remote (Everywhere)
4 years of exp
Contract
About Hashmark Labs Hashmark Labs is a senior-led engineering consultancy building production-grade systems across
AI, Web3 and FinTech
for international clients.
We are looking for an exceptional
Principal AI Engineer
who can independently architect, engineer and deploy sophisticated AI systems, with deep expertise in agentic workflows, retrieval, evaluation and AI-native infrastructure.
This is not a prompt-engineering or prototype-only role. We need an experienced engineer who has
personally shipped production AI systems
and can own complex technical delivery from architecture to scale.
What You'll Build Agentic AI Systems:
Multi-agent architectures, graph-based orchestration, planning, routing, tool calling, memory, queues and human-in-the-loop workflows. RAG & Enterprise Search:
Hybrid retrieval, embeddings, vector databases, metadata-aware reranking, multi-tenant knowledge systems and access control. LLM Evaluation & Observability:
Evaluation datasets, quality metrics, tracing, model benchmarking, regression testing and reliable production monitoring. Multimodal & Document Intelligence:
AI pipelines processing PDFs, tables, images, voice and complex enterprise information with traceable outputs. AI-Native Infrastructure:
Scalable APIs, asynchronous services, distributed workloads, data pipelines, containerized/cloud deployments and cost-efficient inference. End-to-End Technical Ownership:
Translate complex client requirements into robust architectures, production implementations and measurable business outcomes.
What We're Looking For Exceptional Python engineering
with strong backend architecture and distributed-systems fundamentals. Deep production experience with
LLMs, AI agents, tool calling, structured outputs and agent orchestration . Strong expertise in
RAG, vector search, retrieval optimization and enterprise AI systems . Practical experience with
LLM evaluation, observability, reliability engineering and performance optimization . Hands-on experience with
FastAPI, PostgreSQL/pgvector, vector databases, Redis, Kafka, Docker, Kubernetes, AWS or GCP .
Preferred:
Experience building enterprise SaaS, financial AI, underwriting or decision systems, voice agents, multimodal platforms, regulated workflows or AI + blockchain applications.
We value
demonstrated engineering depth and systems ownership over arbitrary years of experience .
How We Work Our engineering philosophy is
evaluation-first and production-focused .
We prioritize measurable accuracy, grounded outputs, system reliability, data security, latency, cost efficiency and maintainable architectures.
You will collaborate directly with international product and engineering teams while retaining hands-on ownership of critical technical decisions.
Engagement & Compensation Type:
Contractual engagement Location:
Fully remote — worldwide Commitment:
Approximately 5–6 hours/day, 5 days/week Schedule:
Flexible working hours Compensation:
Negotiable based on experience, technical expertise and demonstrated production ownership Performance-based revisions:
Compensation may be reviewed and increased based on delivery and project performance, potentially including increases of 50% or more. Such revisions are not guaranteed. International payments:
USDC or INR arrangements may be discussed where applicable Flexibility:
Other professional engagements are permitted, subject to meeting agreed commitments
#J-18808-Ljbffr
About Hashmark Labs Hashmark Labs is a senior-led engineering consultancy building production-grade systems across
AI, Web3 and FinTech
for international clients.
We are looking for an exceptional
Principal AI Engineer
who can independently architect, engineer and deploy sophisticated AI systems, with deep expertise in agentic workflows, retrieval, evaluation and AI-native infrastructure.
This is not a prompt-engineering or prototype-only role. We need an experienced engineer who has
personally shipped production AI systems
and can own complex technical delivery from architecture to scale.
What You'll Build Agentic AI Systems:
Multi-agent architectures, graph-based orchestration, planning, routing, tool calling, memory, queues and human-in-the-loop workflows. RAG & Enterprise Search:
Hybrid retrieval, embeddings, vector databases, metadata-aware reranking, multi-tenant knowledge systems and access control. LLM Evaluation & Observability:
Evaluation datasets, quality metrics, tracing, model benchmarking, regression testing and reliable production monitoring. Multimodal & Document Intelligence:
AI pipelines processing PDFs, tables, images, voice and complex enterprise information with traceable outputs. AI-Native Infrastructure:
Scalable APIs, asynchronous services, distributed workloads, data pipelines, containerized/cloud deployments and cost-efficient inference. End-to-End Technical Ownership:
Translate complex client requirements into robust architectures, production implementations and measurable business outcomes.
What We're Looking For Exceptional Python engineering
with strong backend architecture and distributed-systems fundamentals. Deep production experience with
LLMs, AI agents, tool calling, structured outputs and agent orchestration . Strong expertise in
RAG, vector search, retrieval optimization and enterprise AI systems . Practical experience with
LLM evaluation, observability, reliability engineering and performance optimization . Hands-on experience with
FastAPI, PostgreSQL/pgvector, vector databases, Redis, Kafka, Docker, Kubernetes, AWS or GCP .
Preferred:
Experience building enterprise SaaS, financial AI, underwriting or decision systems, voice agents, multimodal platforms, regulated workflows or AI + blockchain applications.
We value
demonstrated engineering depth and systems ownership over arbitrary years of experience .
How We Work Our engineering philosophy is
evaluation-first and production-focused .
We prioritize measurable accuracy, grounded outputs, system reliability, data security, latency, cost efficiency and maintainable architectures.
You will collaborate directly with international product and engineering teams while retaining hands-on ownership of critical technical decisions.
Engagement & Compensation Type:
Contractual engagement Location:
Fully remote — worldwide Commitment:
Approximately 5–6 hours/day, 5 days/week Schedule:
Flexible working hours Compensation:
Negotiable based on experience, technical expertise and demonstrated production ownership Performance-based revisions:
Compensation may be reviewed and increased based on delivery and project performance, potentially including increases of 50% or more. Such revisions are not guaranteed. International payments:
USDC or INR arrangements may be discussed where applicable Flexibility:
Other professional engagements are permitted, subject to meeting agreed commitments
#J-18808-Ljbffr