Senior AI Engineer | JobSetuu

Description

Job Overview

• Source: Jobicy

Job Description

emerchantpay is a leading global payment service provider and acquirer for online, mobile, in-store and over the phone payments. Our global payments solution is available through a simple integration, offering a diverse range of features, including global acquiring, global and local payment methods, advanced fraud management and performance optimisation. We empower businesses to design seamless and engaging payment experiences for their consumers.

We are looking for a Senior AI Engineer to join our AI Engineering team and help design, build, and roll out production-grade AI solutions, with a strong focus on AI engineering, AI agents, agentic workflows, machine learning, GenAI, and LLM-based applications.

This is a senior individual contributor role within the AI Engineering team. The Senior AI Engineer will work closely with the AI Tech Lead, engineering teams, product stakeholders, data teams, cloud/platform teams, and security teams to deliver reliable AI capabilities into real business systems.

The technology stack is diverse and can include Python (FastAPI/Flask/Django) or equivalent frameworks; React on the frontend side, and various ML/AI frameworks, APIs, cloud-native services, along with modern AI tooling.

The role will have a strong focus on AWS, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS AI/ML services.

Responsibilities

• Design, build, and maintain AI-powered applications, services, and integrations as part of the AI Engineering team.
• Implement solutions focused on AI agents, agentic workflows, automation, LLM-based applications, and AI-assisted business processes.
• Build and integrate AI applications using technologies such as Python (FastAPI/Flask/Django) or equivalent frameworks, React frontends, and relevant AI/ML frameworks.
• Implement AI solutions using AWS AI/ML services, including Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and other AWS services for model hosting, inference, orchestration, data processing, monitoring, and security.
• Work closely with the AI Tech Lead to align on architecture, technology choices, engineering standards, AI patterns, and rollout approaches.
• Provide technical input and guidance to other engineers on AI implementation patterns, code quality, testing, observability, and production readiness.
• Develop and integrate AI agents that interact with internal APIs, business workflows, enterprise systems, knowledge bases, and external tools in a safe and controlled way.
• Build and maintain RAG-based solutions, including document ingestion, chunking, embeddings, vector search, retrieval logic, reranking, and grounding techniques.
• Support the development and deployment of machine learning models and AI solutions into production environments.
• Contribute to ML pipelines and MLOps practices, including data preparation, model training, experiment tracking, model deployment, monitoring, evaluation, and lifecycle management.
• Integrate LLMs through APIs.
• Implement AI evaluation approaches for LLM outputs, RAG quality, agent behavior, model performance, hallucination detection, safety, and reliability.
• Support prompt engineering, prompt versioning, function calling, tool use, memory patterns, guardrails, and LLM application testing.
• Design and consume APIs and contribute to cloud-based, scalable backend architectures.
• Collaborate with product managers, engineers, data scientists, DevOps, security, and business stakeholders to deliver practical AI solutions.
• Write clean, maintainable, testable, and well-documented code.
• Support production rollouts, troubleshooting, monitoring, optimization, and continuous improvement of AI systems.
• Stay current with modern AI technologies, frameworks, models, and engineering practices, and bring practical recommendations to the team.

Requirements

• Minimum 7-8 years of professional experience in software engineering, AI engineering, ML engineering, data science, or related technical roles.
• At least 2-3 years of experience in AI development, ML engineering, or data science, with a demonstrated track record of deploying machine learning models and AI solutions in production environments.
• Strong hands-on experience building production-grade AI, ML, and data-driven systems.
• Practical experience with AI agents, agentic workflows, LLM-based applications, tool-calling architectures, workflow automation, and AI orchestration patterns.
• Strong understanding of modern AI concepts, including deep learning, generative AI, LLMs, embeddings, RAG, LLM fine-tuning, and AI evaluation.
• Strong Python development experience, including experience with Python (FastAPI/Flask/Django) or equivalent frameworks.
• Some experience with React for building user-facing AI tools, internal applications, dashboards, or workflow interfaces.
• Strong knowledge of AWS, including practical experience with cloud-native architectures, Amazon Bedrock, Amazon Bedrock AgentCore, Amazon SageMaker, and related AWS AI/ML services (the more, the better)
• Experience with advanced LLM frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar agent/orchestration frameworks.
• Experience with PyTorch or TensorFlow, and familiarity with Hugging Face Transformers.
• Hands-on experience using LLMs via APIs, such as OpenAI, Anthropic, Gemini, or similar providers.
• Experience with ML pipelines and MLOps, including data preparation, model training, model deployment, experiment tracking, model/version management, monitoring, evaluation, and production support.
• Experience with AI evaluation frameworks, tools, and techniques for assessing LLM outputs, RAG performance, agent behavior, model quality, safety, reliability, and regression over time.
• Knowledge or practical experience with RLHF - human-in-the-loop evaluation, preference data, reward modeling, or feedback-driven model improvement.
• Experience with vector databases and retrieval/search technologies, such as Amazon OpenSearch, Pinecone, pgvector, or similar.
• Experience building RAG systems, including document ingestion, chunking strategies, embeddings, retrieval evaluation, reranking, and grounding techniques.
• Experience with model fine-tuning, embedding models, transformer architectures, open-source LLMs, and model benchmarking.
• Knowledge of API design, microservices, event-driven systems, and cloud-based architectures.
• Good understanding of security and governance requirements for AI systems, including access control, secrets management, data privacy, audit logging, and safe handling of sensitive data.
• Experience working in cross-functional teams with engineers, product managers, data scientists, DevOps, security, and business stakeholders.
• Strong problem-solving skills and ability to turn AI prototypes into reliable, maintainable production systems.
• Strong communication skills and ability to explain technical decisions clearly to both technical and non-technical stakeholders.

Considered as an Advantage

• Experience with Amazon Bedrock Agents, Amazon Bedrock Knowledge Bases, Amazon Bedrock Guardrails, or similar managed AI capabilities.
• Experience with containerization and orchestration, including Docker and EKS/ECS.
• Experience with infrastructure as code using Terraform, AWS CDK, or CloudFormation.
• Experience with data platforms, ETL/ELT pipelines, data lakes, feature stores, and real-time data processing.
• Experience implementing responsible AI controls, AI governance frameworks, safety guardrails, and compliance processes.
• Experience with observability for AI systems, including tracing, cost monitoring, prompt/model analytics, latency tracking, and quality dashboards.
• Experience integrating AI systems with enterprise platforms, internal APIs, CRM/ERP systems, ticketing systems, knowledge bases, and workflow engines.
• Contributions to open-source AI/ML projects, published technical content, conference talks, or patents in AI/ML-related areas.
• AWS certifications, especially in architecture, machine learning, security, or DevOps.
• Experience in fintech.

Benefits

• Fast-growing payment company;
• Excellent working conditions, casual atmosphere, and state-of-the-art hardware;
• Modern, challenging, constantly growing business;
• Professional development – books, trainings, certifications, etc.;
• Team buildings and fun activities;
• 25 days paid holiday, 1 day for every 2 years with us;
• Fully distributed and remote.

If you are interested, please apply with your CV in English only. Only short-listed candidates will be contacted.

Personal data of the applicants will be processed in strict confidentiality by emerchantpay ltd. UIC 175117520 solely for the purposes of selection and recruitment and will not be transferred to other data controllers unless required by law. Applicants provide their personal data on a voluntary basis and will have the right to access and correct their personal data within a reasonable time upon filing a written request.

emerchantpay is an equal opportunity employer. We appreciate people with different backgrounds and mindsets, and we honor diversity and inclusion.

Expert Career Tips for Senior AI Engineer Roles

To succeed in a competitive market as a Senior AI Engineer, you need more than just technical skills. Here are some expert strategies to elevate your profile:

• Build a Strong Portfolio: For technical roles, a clean GitHub or a personal project site is essential. For non-technical roles, a case study portfolio demonstrating problem-solving and impact is equally valuable. Show, don't just tell, what you have achieved in your previous positions.
• Master the Narrative: When interviewing, use the STAR method (Situation, Task, Action, Result) to structure your answers. Quantify your results wherever possible—mentioning "increased efficiency by 20%" is much more impactful than saying "improved efficiency."
• Continuous Learning: The industry moves fast. Whether it's staying updated with the latest AI tools or mastering a new management methodology, continuous professional development is key. Consider obtaining industry-recognized certifications that align with Senior AI Engineer requirements.
• Networking: Connect with other professionals in similar roles. Join online communities, attend webinars, and engage in meaningful discussions on professional social networks. Often, the best opportunities come through referrals and community engagement.
• Soft Skills Matter: Communication, empathy, and leadership are often the deciding factors between two equally qualified technical candidates. Cultivate these skills as they are universally valued across all industries and seniority levels.

Additionally, research the specific company's culture and values. Tailoring your application to show how you align with their mission can significantly increase your chances of moving forward in the process.

Salary & Compensation

Salary not disclosed; typically competitive for the role.

Work Arrangement

Type: On-Site

Standard business hours at the office.

Comprehensive Application Strategy & Hiring Process

Applying for a new role is a marathon, not a sprint. Follow this strategic approach to maximize your success rate:

1. Initial Research & Tailoring

Don't send the same resume to every employer. Spend at least 30 minutes researching the company. Look for recent news, their product roadmap, and their team structure. Modify your summary and core competencies to reflect the specific keywords found in the job description.

2. The Perfect Cover Letter

If the application allows for a cover letter, use it to tell a story that your resume cannot. Explain why you are passionate about this specific company and how your unique background makes you the perfect fit for the challenges they are currently facing.

3. Navigating the Multi-Stage Interview

Most modern hiring processes involve 3-5 stages. This typically includes a recruiter screen, a technical or skill-based assessment, a peer interview, and a final leadership round. Prepare for each stage differently: focus on enthusiasm and fit for the recruiter, technical depth for the assessment, and strategic vision for the leadership round.

4. Post-Interview Follow-Up

Always send a personalized thank-you note within 24 hours of each interview. Reference a specific topic discussed during the call to demonstrate your active listening and genuine interest in the role.

By following these steps, you demonstrate a high level of professionalism and attention to detail that sets you apart from the average applicant.

Typical Interview Process

1. Resume screening
2. HR call
3. Skill interview
4. Final manager interview
5. Offer

Tip: Research the company's products and culture.

Global Market Intelligence & Relocation Insights

At JobSetuu, we specialize in helping talent navigate the global job market. Here is what you need to know about the current landscape in Global and beyond:

The demand for skilled professionals is increasingly borderless. For roles based in Global, understanding the local cost of living, visa requirements (if applicable), and cultural nuances is vital. If this is a remote role, consider the time zone alignment and the asynchronous communication culture of the hiring organization.

Relocation Support: Many forward-thinking companies offer relocation packages that include moving stipends, temporary housing, and legal assistance with work permits. When evaluating an offer, look beyond the base salary—consider the total compensation package, including equity, bonuses, and healthcare benefits.

Work-Life Balance Trends: Hybrid and remote work have become standard in many regions. Research the local labor laws and common practices regarding work hours and vacation time to ensure the role aligns with your lifestyle goals.

Leveraging JobSetuu's tools can help you compare salaries across different cities and understand the "purchasing power" of your potential offer, ensuring you make an informed decision for your long-term career path.

Skills & Competency Roadmap for Professional Development

To remain competitive in Professional Development, we recommend focusing on the following core competencies over the next 12-18 months:

• Technical Mastery: Deepen your expertise in the core tools and languages relevant to your field. For developers, this might be cloud architecture; for marketers, it might be data-driven attribution modeling.
• AI Augmentation: Learn how to leverage generative AI and automation tools to increase your productivity. Understanding how to integrate these technologies into your workflow is becoming a non-negotiable skill.
• Leadership & Strategy: Even in individual contributor roles, the ability to think strategically and lead projects from inception to completion is highly valued. Focus on stakeholder management and high-level project planning.
• Data Literacy: The ability to interpret data and use it to drive decisions is essential across all business functions. Familiarize yourself with data visualization and basic analytical concepts.

By investing in these areas, you not only prepare yourself for the role you are applying for today but also build a resilient foundation for the opportunities of tomorrow.

Apply via JobSetuu

Discover your next career milestone on JobSetuu. This Senior AI Engineer position is part of our commitment to bringing you the most relevant and high-impact job openings globally. At JobSetuu, we simplify your job search by aggregating premier listings and providing the tools you need to stand out. Don't miss the chance to elevate your professional journey—explore more opportunities and career insights on our platform today.