Senior AI Engineer

HFM

Senior AI Engineer at HFM, an international multi-asset broker, developing AI products and agentic workflows using microservices and managed services.

Posted 7/29/2026full-timeLarnaca • 🇨🇾 CyprusSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in mentoring AI engineering teams and architecting scalable AI solutions using FastAPI, PostgreSQL, and AWS. Proficient in developing high-performance microservices and implementing LLMOps practices to enhance internal AI initiatives.

Highest-signal resume keywords

Mentoring AI Engineering TeamsFastAPI DevelopmentPostgreSQL and PgvectorAWS InfrastructureLLMOps Practices

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Applicant Tracking System Keywords

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Hard Skills

PythonFastAPIPostgreSQLPgvectorAWSMulti-Agent FrameworksDockerKubernetesCI/CDREST API Development

Soft Skills

Strong Communication SkillsStakeholder ManagementProduct-Oriented Mindset

Tools & Technologies

Amazon Bedrock AgentCoreOpenTelemetryCloudWatchLangGraphCrewAIAutoGenLlamaIndexMCP

Certifications & Qualifications

AWS Certified Machine Learning – SpecialtyAWS Certified Solutions Architect

Industry Keywords

FinTechFX TradingFinancial Services Operations

Tech Stack

Tools & technologies

AWSDockerGoKubernetesMicroservicesPostgresPython

About the role

Key responsibilities & impact

Mentor a team of 3 AI Engineers, guiding technical architecture, code quality, sprint delivery, and skill development for internal AI initiatives.
Design and deploy internal AI products and agentic workflows using a hybrid stack of custom microservices (FastAPI) and managed services (Amazon Bedrock AgentCore).
Develop high-performance REST APIs and asynchronous microservices in FastAPI to orchestrate agent execution, connect internal tools, and handle real-time data flow.
Architect scalable data layers and persistent state stores utilizing PostgreSQL and pgvector for agent memory, transactional records, and enterprise retrieval.
Build and optimize multi-agent orchestration systems leveraging task decomposition, state management, tool integrations via Model Context Protocol (MCP), and structured reasoning.
Partner cross-functionally with internal stakeholders and department heads to identify operational bottlenecks and translate business requirements into high-impact AI automations.
Implement robust AI guardrails and evaluation suites (using Bedrock Guardrails, schema validation, and latency/accuracy telemetry) to guarantee safe and reliable internal agent execution.
Establish LLMOps standard practices, including RAG pipeline tuning, prompt versioning, OpenTelemetry tracing, and latency monitoring across custom microservices and AWS infrastructure.

Requirements

What you’ll need

Bachelor's degree in Computer Science, Artificial Intelligence, Software Engineering, or related field.
5+ years of backend software development experience, including 3+ years building and deploying LLM and agentic AI applications.
Strong proficiency in Python, FastAPI, PostgreSQL (including pgvector), and AWS.
Experience with Amazon Bedrock AgentCore, multi-agent frameworks (e.g., LangGraph, CrewAI, AutoGen, LlamaIndex, MCP), Docker, Kubernetes, and CI/CD.
Familiarity with LLM observability and monitoring tools (e.g., OpenTelemetry, LangSmith, Phoenix, CloudWatch).
Proven experience mentoring software/AI engineering teams.
Strong communication skills and full professional proficiency in English.
Product-oriented mindset with the ability to build scalable, user-focused internal tools and workflow automations.
Strong communication and stakeholder management skills, with the ability to explain technical concepts to non-technical audiences.
Nice to have: AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect.
Experience using pgvector or relational data pipelines in production RAG systems.
Experience building internal developer platforms, enterprise workflow automation, or administrative AI assistants.
Familiarity with FinTech platforms, FX trading, or financial services operations.
Knowledge of systems languages such as Go or C++.

Benefits

Comp & perks

Hybrid Work Model (2 days working from home)
Monthly Wolt Vouchers
Comprehensive Health & Life Insurance
Provident Fund (Upon completion of the trial period)
Summer Short Fridays (August)
Additional Paid Annual Leave (up to 30 days, based on years of service)
Birthday Leave
Training & Education Allowance
Udemy Business access
Gym Membership
Referral Bonus Program
Additional Support
Visa Sponsorship and Relocation Assistance