Jobs ML / AI

WPP is the creative transformation company. We use the power of creativity to build better futures for our people, planet, clients and communities. For more information, visit wpp.com.
WPP Media is WPP’s global media collective. In a world where media is everywhere and in everything, we bring the best platform, people, and partners together to create limitless opportunities for growth. For more information, visit wppmedia.com

About Choreograph: A Leading WPP Media Brand
Choreograph is WPP’s global data products and technology company. We’re on a mission to transform marketing by building the fastest, most connected data platform that bridges marketing strategy to scaled activation.

We work with agencies and clients to transform the value of data by bringing together technology, data and analytics capabilities. We deliver this through the Open Media Studio, an AI-enabled media and data platform for the next era of advertising.

We’re endlessly curious. Our team of thinkers, builders, creators and problem solvers are over 1,000 strong, across 20 markets around the world.

Essential

• GCP (esp Vertex, BigQuery, Model Registry)
• Python (needs to be very strong)
• Docker
• FastAPI (or similar)
• SQL
• Terraform
• Strong understanding or even hands-on ML experience
• Package management (uv is cool, but don’t mind others e.g. poetry; we work with a whole range here)
• Scalable experimentation & model tracking (no specific tech as we’re using native GCP logging and metadata store atm, but as we mature open-source tech e.g. MLFlow will be brilliant), and Scalable ML deployment experience (i.e. standing up an inference endpoint for hardly no traffic doesn’t count)

Desirable:

• FTI (Feature/Training/Inference) framework
• Common ML frameworks (such as PyTorch, Sklearn)

Bonus:

• RAG
• LLM orchestration tool e.g LangGraph,
• Reinforcement Learning tools e.g. OpenaiGym, RLib

Job Responsibilities

The Machine Learning Engineer is responsible for deploying and maintaining the algorithms developed by data scientists. This role will be part of the Optimize Data Science team (3 FTEs plus seconded team of 5 FTEs)

While Data Scientists focus on research and model development, the ML Engineer is responsible for the technical infrastructure, scaling, performance optimization, and maintenance of the models. Their work involves implementing, testing, deploying, and monitoring the models in a production environment.

• Model deployment: Takes models (brand new or improvements to existing models) developed by data scientists and builds the software and infrastructure to deploy them into a live production environment.
• Performance optimization: Optimizes code for latency and efficiency across different hardware, like CPUs and GPUs, and overall quality (e.g. readability, maintainability, reliability and so forth).
• System design & scaling: Designs, builds, and maintains the technical components that integrate into existing software to train, deploy, and scale ML models. (N.B. There will be opportunity to work on new systems from ground up later in the year, too.)
• Monitoring and maintenance: Implements logging and monitoring to track model performance, identifies and fixes bugs (in collaboration with wider teams if appropriate), and performs necessary updates and improvements.
• Collaboration: Works closely with data scientists and wider engineering teams to understand the model and help convert it into a production-ready system.
• Experimentation: Design, build and deploy technical components based on the methodologies designed by Data Scientists to enable scalable experiments, model evaluation and visualisation of results.
• Up-skilling DS in ML Engineering and AI innovations: Support DS team to utilise modern and cloud-based (esp GCP) technologies for development (e.g. Vertex, Docker, BigQuery, Dev Containers, Ray and others) to expedite and innovate the entire development lifecycle

Tech Stack:
• Python (5+ years)
• Strong working knowledge of SQL for data extraction and analysis (3+ years preferred)
• API integration with LLMs: OpenAI, Azure OpenAI, Claude
• ML/LLM architectures: RAG, embeddings, retrievers
• DevOps/MLOps: Docker, deployment of models on dedicated servers or cloud infrastructure
• Nice to have: FastAPI, vector databases (FAISS, Weaviate), prompt engineering
• Tools under consideration: LangChain or similar frameworks for rapid prototyping of LLM-based pipelines

Responsibilities:
• Design and build a production-grade LLM+RAG system for contextual data analysis
• Integrate external data sources and tools into the assistant’s knowledge base
• Set up and fine-tune LLMs on dedicated infrastructure or in the cloud
• Develop complex multi-step agents and orchestrated pipelines
• Build integrations with different platforms via API
• Ensure performance, scalability, and CI/CD automation of the ML systems

Requirements:
• Proven experience building real-world AI/ML products
• Deep understanding of the full machine learning lifecycle
• Experience working with popular LLM APIs and frameworks
• Ability to work independently and propose technical solutions
• Hands-on experience with LLM+RAG design patterns and prompt engineering

We Offer:

• A fully remote role with flexible engagement (full-time or contract) – you can also choose to work in the office or in a hybrid format if you prefer
• An opportunity to influence and shape the architecture and overall direction of the AI product
• Work at a large product company with a wide range of in-house developed solutions, many of which have been leading the market for over 15 years
• Access to diverse internal projects, enabling you to develop various skill sets and show your potential in different areas
• A competitive compensation package that reflects your experience and skills, with opportunities for review based on performance

About ChatRevenue.ai 

ChatRevenue.ai was founded by Ratmir Timashev (founder of Veeam) and Vlad Voskresensky (founder of Revenue Grid). 

We are building an agentic sales automation for post-CRM era. The AI-native product combines structured and unstructured data, LLM workflows, and agentic automation to help sales people to focus properly, work more efficiently and automate repetitive sales processes. 

The product is already being tested by early customers. We are growing the core engineering team and looking for people who want to work on real product systems with direct impact on architecture, quality, and speed of delivery. 

About the role 

We are looking for a Senior Generative AI Engineer with 5+ years of commercial Python experience and hands-on experience building production GenAI / RAG systems. 

This is a hands-on engineering role focused on: 

• Python backend  
• LLMs and RAG  
• AI Copilot / assistant workflows  

We are looking for someone who can take a problem, propose a practical solution, and move it to production. 

What you will do 

• Design, build, deploy, and maintain production GenAI / RAG pipelines  
• Integrate LLMs and agentic assistants into product workflows  
• Improve latency, reliability, scalability, and cost-efficiency of AI systems  
• Evaluate and implement new GenAI, multi-agent, and orchestration frameworks  
• Contribute to architecture and technical decisions across the platform  

What we are looking for 

• 5+ years of commercial Python experience 
• Strong backend experience with FastAPI, APIs, webhooks, Docker, Kubernetes, Git, and CI/CD 
• Commercial experience with LLMs in production 
• Strong hands-on experience with RAG, retrieval workflows, embeddings, and vector search 
• Experience with LangChain, LangGraph, LangSmith, or similar frameworks 
• Good knowledge of SQL, Pandas, ETL, data cleaning, and ingestion pipelines 
• English: B2 or higher 
• High ownership, independence, and product mindset 

Nice to have 

• Multi-agent LLM systems 
• Knowledge graphs, graph databases, or graph-based retrieval 
• Kafka, Azure Event Hub, or other event-driven systems 
• Azure cloud infrastructure 
• Production ML / NLP systems 
• Evaluation, tracing, observability, and optimization of AI pipelines 
• Graph algorithms or tools like NetworkX 

What we offer 

• Remote-first setup 
• Flexible schedule aligned with European time zones 
• A core engineering role in an AI-native product 
• Direct influence on technical decisions and architecture 
• Real ownership and visible product impact 

If you have strong Python backend fundamentals and real production GenAI / RAG experience, we’d love to talk. 

The Senior ML Engineer will spearhead the end-to-end development, deployment, and stewardship of machine-learning solutions that power credit-risk, collections-strategy, conversion-optimisation, and fraud-detection processes in MD Finance. Working hand-in-hand with Risk, Product, Operational, and other teams, the role will translate business goals into robust models, ensure their ongoing performance, and identify new AI/ML opportunities that raise the company’s bottom line.

Professional qualifications
 

• 7+ years’ experience in Machine Learning / Data Science, with 3+ years in credit-lending organisations and/or banks.
• Demonstrated delivery and production of Probability-of-Default (PD)
  models, credit-limit strategies, fraud-detection, conversion-uplift, and collections-optimisation models.
• Advanced Python proficiency and solid grasp of modern ML algorithms, feature
  engineering, and model-evaluation best practices.
• Ability to write, structure, and optimise complex SQL queries.
• Deep understanding of the credit lifecycle, especially online lending workflows.
• Proven skill in sourcing, cleansing, and generating features from data sets.
• Comfortable setting up and maintaining modelling environments (local, cloud, or
  on-prem).
• Detail-oriented, accountable, and committed to both team and individual targets.
• English: Intermediate (B1) or higher.
   

Preferred / bonus qualifications

• Practical experience with LLM solutions:
• Using commercial APIs (e.g., OpenAI, Anthropic, etc.).
• Self-hosting of open-source models
• Fine-tuning of open-source models.
• Building voice chatbots.
• Building RAG chatbots.
• Experience with Computer Vision models for document or image processing.
• Building ML pipelines and deploying models to production.
• Creating executive dashboards and model reports in Power BI.

Main responsibilities

• Design, train, and deploy probability of default models.
• Build credit-limit strategies.
• Discover and scope AI/ML opportunities that boost efficiency and revenue of the company, including collections optimisation, fraud control, conversion lift, etc.
• Analyse data sources and engineer features for modelling.
• Produce and update internal model documentation.
• Implement model monitoring.
• Plan and execute A/B tests.
• Build Computer Vision pipelines to automate lending workflows.
• Develop LLM-based solutions that streamline internal processes or enhance customer experience.

Expected results

• Implemented probability of default models and credit-limit strategies.
• Launched A/B tests for models that potentially can boost the efficiency and/or revenue of the company.
• Thorough, audit-ready documentation for models.

What We Offer

• Join a fast-scaling FinTech company where your decisions shape the business and your contributions truly matter.
• Enjoy 20 paid days off annually, flexible scheduling, and a supportive, people-first culture.
   
• Learning, sports, and medical insurance compensation.
• Work in an international, agile team with ambitious goals, modern tools, and a strong sense of purpose.

We are looking for AI-driven Software Engineer to join our long-term project.

Responsibilities:

• Design practical, scalable software architectures
• Translate business requirements into clear technical solutions
• Define data models, APIs, integrations, and system structures
• Contribute across the full lifecycle — from idea to production

You’ll be part of a small, fast-moving team where ownership and initiative are highly valued.
 

Who you are

• You take ownership and enjoy responsibility
• You communicate clearly with both technical and non-technical stakeholders
• You’re comfortable in a fast-paced, evolving environment

You also:

• Think in systems and trade-offs, not rigid rules
• Take a hands-on, proactive approach
• Balance structure with pragmatism
• Stay curious and keep up with new technologies
• See AI as a practical tool, not just a trend
   

Requirements:

You don’t need to master everything, but you’re familiar with or interested in:

• APIs and system integrations
• Modern architectures (React, React Native, TypeScript, Next.js, Supabase)
• AI-assisted development tools (e.g., Cursor, Claude Code)
• Automation tools (e.g., n8n)
• No-/low-code platforms (e.g., Bubble.io — a plus)
  
  Location Belgium must have!
   

👉 What matters most: understanding core principles, not just tools.

About the Role

We are looking for a highly experienced Staff Engineer to build and own a next-generation production intelligence platform at the intersection of AI, infrastructure, and data systems.

This role goes beyond traditional backend or data engineering — it focuses on designing a knowledge graph layer that enables AI systems to understand and reason about complex production environments in real time.

You will be one of the first engineers in this domain area, with full ownership of architecture and key technical decisions.

What You Will Do

• Design and own a production knowledge graph architecture (schema, entities, relationships, temporal modeling)
• Build data ingestion pipelines from multiple sources (CI/CD, infrastructure, cloud systems, streaming platforms)
• Define strategies for handling high-volume, high-cardinality production data
• Develop a semantic layer enabling intelligent querying and reasoning over the system
• Ensure data consistency, quality, and long-term coherence
• Work closely with engineering leadership on architecture and system design
• Validate solutions against real-world production environments

Requirements

• 10+ years of experience in backend or systems engineering
• Strong hands-on experience with graph databases (Neo4j preferred)
• Proven experience building scalable data ingestion pipelines
• Deep understanding of distributed systems and cloud-native infrastructure
• Experience with Kubernetes, CI/CD, and modern DevOps practices
• Strong data modeling skills (entities, relationships, versioning, schema design)
• Ability to make architectural decisions independently in a fast-paced environment

Nice to Have

• Experience with infrastructure graph tools (e.g., Cartography or similar)
• Experience working with AI/ML systems or LLM-based architectures
• Familiarity with event-driven systems (Kafka, streaming)
• Background in knowledge graphs or semantic systems

Tech Stack

Neo4j, Python, Kafka, Kubernetes, PostgreSQL, Graph-based tooling, cloud infrastructure

What Makes This Role Unique

This is not a typical backend or data role. You will be building the core knowledge layer that enables AI systems to reason about production environments.

You will:

• Own the architecture from day one
• Make high-impact technical decisions
• Work on a product that sits at the intersection of AI + infrastructure + data

Hiring Process

• 2–3 interview stages
• Technical + system design focus

Lumitech is a Dubai-based software engineering company building complex digital products for enterprise clients across the UAE and Saudi Arabia.

We are expanding our AI capabilities and looking for a strong Applied AI Engineer who can design and build real production AI systems — not just integrate APIs. Our team already handles full-stack engineering. 

You will design and implement AI systems for enterprise products, including:

• Retrieval-Augmented Generation (RAG) platforms
• Enterprise knowledge assistants
• AI agents and workflow automation
• Document intelligence systems
• Internal copilots for large organizations

Typical projects include legal AI assistants, enterprise knowledge systems, and AI-driven automation tools.

This is not just an individual contributor role. We are looking for a person around whom we can build our AI direction — someone who can take ownership, shape architecture, and eventually grow and lead an AI team within Lumitech.

Our goal is to find a long-term partner, not just an employee — someone who will grow with us for years to come.

As a Backend Developer, you will actively shape the future of AI-driven applications. Working within an agile product team, you will see the direct impact of your work on customers and contribute to one of the most exciting technology areas today.

Even if you don’t meet all the listed qualifications, we encourage you to apply. Curiosity, integrity, and strong ownership matter more than ticking every box.

Responsibilities

• Take ownership of architectural decisions and drive the technical evolution of the backend stack
• Design and manage complex database systems, including data modeling and migration strategies
• Build and maintain scalable, secure, and highly available services and APIs
• Collaborate closely with internal stakeholders to design and deliver new features
• Conduct code reviews and support team members through mentoring and technical guidance

Requirements

• 5+ years of experience in C#/.NET development
• Strong understanding of software architecture, API design, distributed systems, and data modeling
• Experience with CI/CD pipelines and Docker-based deployments
• Excellent written and spoken communication skills in German and English
• High level of ownership when working with ambiguous requirements and complex challenges

Nice to Have

• Experience in mentoring or leading developers (technical or people management)
• Openness to and hands-on experience with AI coding agents

What You’ll Get

• Flexible working hours with the option to work remotely or in a modern office environment
• A collaborative, cross-functional product team with short decision-making paths
• Regular team events and a pragmatic, fast-paced work culture
• Opportunity to work on impactful AI products in an ambitious startup environment

We're partnered with an AI company that cleans up and enriches messy product data (PDFs, spreadsheets, images, CAD files) turning it into clean, marketplace-ready catalogs for brands and retailers selling on Shopify, Amazon, Walmart, and large PIM/ERP systems. We're helping the company bring on a few more engineers – one of the positions is this Data Scientist one.

Stack: Python / FastAPI on the backend, React / Next.js on the frontend, LLMs throughout. 

Engagement: Full-time, fully remote.

=====

You'll own the ML that powers the enrichment, classification, and validation pipelines — the core of the product. Real data, large scale, noisy inputs, a lot of LLM work alongside classical ML.

What you'll do

• Build classification, extraction, and enrichment models over product data (text, images, metadata)
• Design and ship LLM pipelines – RAG, extraction, validation – in production, not just notebooks
• Run experiments and build evaluation frameworks you actually trust
• Work with engineering to deploy models, not hand them off

We're looking for

• 4-7+ years in data science / ML
• Strong Python (pandas, sklearn, PyTorch, TensorFlow, etc)
• Real experience with LLMs, NLP, CV
• Solid instincts on model evaluation and data quality
• You've worked on messy real-world data and know how to handle it
• You're comfortable working with a significant overlap with the US Pacific Time
• Your conversational English is strong (you will be speaking with your teammates daily)

Nice to have

• Product / catalog / e-commerce data experience
• Experience with vector DBs, embeddings, RAG in production

Location: EU
Workload: Full-time
Duration: Long-term

About the role

We are looking for a Senior AI Solution Architect with strong experience in the Microsoft ecosystem and solid expertise in designing enterprise-grade AI solutions. In this role, you will lead architecture decisions for modern AI-driven applications, including solutions based on RAG patterns, Azure AI services, and generative AI technologies.

You will work in a consulting-style environment, helping shape scalable, secure, and production-ready architectures for clients across the EU.

Key responsibilities

• Design end-to-end architecture for AI and cloud-native solutions on Azure
• Lead implementation of solutions leveraging RAG, Azure OpenAI, and related AI services
• Define scalable application architecture using Azure PaaS services
• Integrate AI capabilities such as search, OCR, vision, and content enrichment into business solutions
• Collaborate with engineering and DevOps teams to ensure reliable delivery pipelines
• Support architectural governance, technical decision-making, and best practices
• Translate business requirements into robust technical designs

Required skills and experience

• 8+ years of overall experience with the Microsoft application stack
• Proven background as an Azure Architect in a consulting environment
• Hands-on experience with AI solution implementation
• Strong understanding of prompt engineering techniques
• Experience implementing Retrieval-Augmented Generation (RAG) solutions
• Strong knowledge of:
  • Architecture design
  • Azure PaaS: Container Apps, Functions, Web Apps, Storage
  • Azure Cognitive Services: Azure AI Search, OCR, Vision, etc.
  • Azure AI Foundry or Azure OpenAI
  • Microsoft Fabric, Azure Data Factory, or Databricks
  • Azure DevOps Pipelines or equivalent CI/CD tooling
  • .NET 6+ or Python
• Native-level English

Nice to have

• Experience with Azure OpenAI or other generative AI technologies such as GPT
• Microsoft certifications such as:
  • AZ-900
  • AZ-204
  • AZ-300
  • AZ-400
  • DP-900
  • AI-102

What we offer

• Long-term full-time engagement
• Opportunity to work on modern enterprise AI initiatives
• International environment with EU-based delivery
• High-impact architectural role in a growing AI space

Remote · Contract · EU time zone preferred · Capacity: propose what the scope needs.

We pay for outcomes, not hours. You own the pipeline; we own the product.

Contract engagement, scope-driven. Propose your own structure — trial period, fixed-price milestones, retainer, or a hybrid. Whatever fits how you actually work.
 

We're building the definitive structured quality, safety & value dataset for the global supplement market. Two customers: consumers (React Native app) and AI systems (structured API). The data pipeline IS the product. 
 

What you deliver:

A pipeline that generalizes across our module types: boolean classification, multi-class classification, characteristic extraction, entity extraction, entity linking/grouping, and structured attribute profiling.
Core deliverables:

• Golden-record schema and workflow that works for any of the above, co-built with our regulatory SME
• Eval harness in CI — accuracy reported on every commit, per module
• Versioned prompt/rule system with rollback
• Measured, reproducible accuracy numbers on held-out sets, per module, against written gates
• A workflow our SME can operate without a developer in the loop

Success = numbers we trust, on sets we trust, with a pipeline that won't silently regress — and a framework that scales to every remaining module without rework.
 

Stack: Python, DSPy, vLLM, PaddleOCR, RT-DETR, open-weight VLMs and LLMs. Laravel/Filament backend (you consume it, our backend team maintains it).
 

Required:

• 3+ years shipping production ML/data pipelines — not research
• Prompt optimization at scale: DSPy / BootstrapFewShot / MIPROv2, or equivalent (LangChain + pytest eval harness counts)
• VLM inference serving (vLLM / TGI / llama.cpp)
• OCR pipelines wired to detection models
• Eval-first: you ship the harness before the pipeline
• Clean Python, git discipline, versioned prompts
• You've maintained a pipeline in production for 12+ months (not just built and handed off

Not a fit if you:

• Propose RAG for structured extraction
• Want to fine-tune before prompt-optimizing
• Want to pre-train a domain model
• Need a PM to translate goals into tickets
   

Nice to have: Comfortable reading enough PHP/Laravel to integrate with our Filament admin.

We provide: 

• Direct CEO collaboration — fast decisions.
• Regulatory SME owns golden records and sign-off
• Backend team handles schema and infra
• Written acceptance criteria before each module
   

Compensation: fixed-price per milestone, or retainer. Propose your structure with application.
 

How to apply?

Fill out questioner below and submit.

NB. No cover letters. No AI-generated applications — we'll notice.

You will be a player in the AI-Data Delivery Pod, contributing to enterprise solutions that turn complex domain documentation into machine-usable intelligence. The focus is on building “Self-Evolving Knowledge Bases” that power intelligent assistants and decision-support systems. You will operate within a high-speed delivery environment where the synergy between human expertise and agentic execution is the primary driver of success, all integrated within the client’s existing Azure infrastructure.

We are looking for a Trainee AI / ML Engineer (Generative AI) to join our AI & Data practice. This role is designed for an early-career, AI-native engineer who is comfortable using modern GenAI tools and wants to grow on real projects under the guidance of senior ML / AI experts.

You will help rapidly prototype ideas, build small PoCs, and experiment with solutions based on large language models (LLMs) and other generative AI techniques. Your focus will be to implement well-defined tasks, run experiments, and use GenAI tools to speed up coding, analysis, and documentation – while learning how to do this safely and reliably in production-oriented environments.

Requirements

• Some experience in software development, data science, or ML (commercial experience, internships, or strong personal projects).
• Solid basic skills in Python and core data/ML libraries (e.g., NumPy, pandas, Scikit-learn).
• Understanding of fundamental ML concepts (datasets, train/validation split, basic metrics, overfitting).
• Practical familiarity with generative AI / LLMs:
   – regular use of tools like ChatGPT, Copilot or similar for coding and data tasks;
   – basic understanding of prompts, context windows, and common limitations (hallucinations, privacy, bias).
• High motivation to learn, ask questions, and accept feedback.
• Organized and responsible: able to follow a spec, document work, and meet agreed timelines.

Preferred Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, AI, or a related field.
• Course work, pet projects, or a thesis related to ML, NLP, or Generative AI.
• Basic exposure to RAG, embeddings, or vector databases (from articles, courses, or small experiments).
• Familiarity with at least one cloud platform (Azure, AWS, or GCP) from labs or online courses.
• Experience with Jupyter notebooks, Git, and simple CI/CD or MLOps tools (even at a “student project” level).
• Participation in hackathons, Kaggle competitions, or open-source projects.

Responsibilities: 

• Rapidly prototype and iterate on ML / GenAI ideas in notebooks and scripts, turning high-level concepts into small PoCs and demo flows.
• Implement well-defined tasks in Python (data preprocessing, basic model training, evaluation scripts, and utility functions) as part of prototypes and production-oriented components.
• Assist in experiments with LLMs and GenAI APIs (prompt variants, small workflows, simple RAG setups) under the guidance of senior engineers, proactively suggesting new options and documenting what works and what doesn’t.
• Use GenAI tools to support your daily work (coding, tests, documentation, exploratory analysis) and capture key findings in a clear, structured way.
• Prepare and clean datasets, run experiments, and summarize results in concise formats (tables, short reports, simple dashboards).
• Help build and maintain demo environments and internal PoCs for ML / GenAI use cases, keeping them reproducible and easy to reuse.
• Follow coding standards, version control practices, and basic quality processes (code reviews, simple tests, logging).
• Actively learn from senior team members, ask questions, and gradually take ownership of small components within larger solutions.

What is in for You: 

Culture of caring. At GlobalLogic, we prioritize a culture of caring. Across every region and department, at every level, we consistently put people first. From day one, you’ll experience an inclusive culture of acceptance and belonging, where you’ll have the chance to build meaningful connections with collaborative teammates, supportive managers, and compassionate leaders. 

Learning and development. We are committed to your continuous learning and development. You’ll learn and grow daily in an environment with many opportunities to try new things, sharpen your skills, and advance your career at GlobalLogic. With our Career Navigator tool as just one example, GlobalLogic offers a rich array of programs, training curricula, and hands-on opportunities to grow personally and professionally.

Interesting & meaningful work. GlobalLogic is known for engineering impact for and with clients around the world. As part of our team, you’ll have the chance to work on projects that matter. Each is a unique opportunity to engage your curiosity and creative problem-solving skills as you help clients reimagine what’s possible and bring new solutions to market. In the process, you’ll have the privilege of working on some of the most cutting-edge and impactful solutions shaping the world today.

Balance and flexibility. We believe in the importance of balance and flexibility. With many functional career areas, roles, and work arrangements, you can explore ways of achieving the perfect balance between your work and life. Your life extends beyond the office, and we always do our best to help you integrate and balance the best of work and life, having fun along the way!

High-trust organization. We are a high-trust organization where integrity is key. By joining GlobalLogic, you’re placing your trust in a safe, reliable, and ethical global company. Integrity and trust are a cornerstone of our value proposition to our employees and clients. You will find truthfulness, candor, and integrity in everything we do.

Project Overview
The project focuses on building a production-ready enterprise AI chatbot on SAP BTP Kyma, leveraging modern AI and backend technologies such as SAP Joule, Node.js/TypeScript, NestJS, mastra AI, OpenSearch, and SAP HANA.

The platform emphasizes AI architecture, reliability, and scalability, enabling advanced RAG pipelines, agent-based capabilities, and secure enterprise integrations. Strong engineering practices, observability, and AI quality evaluation are key success factors.


Role Responsibilities
As an AI Developer, the consultant will:

• Design and implement agentic AI capabilities, including tool/function calling, planning, memory/state handling, safety guardrails, and failure recovery
• Build and optimize advanced RAG pipelines on OpenSearch (hybrid retrieval, embeddings, rerankers, query rewriting, retrieval evaluation)
• Develop and maintain API and MCP integrations, including MCP servers/clients, capability manifests, and clean, versioned REST/WebSocket APIs
• Create and iterate prompt strategies, including tool-aware prompting, structured outputs (JSON), and prompt governance
• Establish AI quality evaluation and testing, using tools such as RAGAS and/or DeepEval, including regression testing and red-teaming
• Implement secure and performant data access with SAP HANA using TypeORM
• Ensure observability and performance monitoring using Dynatrace (latency, cost, quality, SLIs/SLOs)
• Build and maintain CI/CD pipelines for Kyma/Kubernetes environments

Collaborate closely with product and architecture teams, document designs, and participate in technical reviews

Mandatory Skills and Experience — MUST HAVE !!!

• Proven production AI adoption, including agentic or advanced RAG solutions with measurable impact
• Strong experience with advanced RAG on OpenSearch (vector and hybrid search, indexing strategies, retrieval evaluation)
• Experience with agent frameworks and patterns (multi-step planning, tool integration, memory/state, guardrails)
• Hands-on experience with AI evaluation and testing (RAGAS, DeepEval, or similar)
• Strong Node.js and TypeScript skills; solid API engineering (REST/WebSockets, OpenAPI, versioning)
• NestJS experience in enterprise-grade services
• Experience with MCP integrations in production or strong evidence of rapid ramp-up capability
• Strong experience with SAP HANA and TypeORM
• CI/CD experience for containerized applications on Kyma/Kubernetes
• Dynatrace experience for observability and performance monitoring
• Security-first mindset (OAuth2/OIDC, JWT, RBAC, secure secrets handling)
• Ability to learn quickly and work within complex enterprise architectures
• Fluent English
  
  
  Nice to Have
• SAP ecosystem knowledge; SAP Joule experience is a plus
• Experience with mastra AI or similar AI orchestration frameworks
• Advanced prompt engineering skills and prompt governance workflows
• Experience integrating AI into web frontends (streaming responses, SSE, rate/cost controls)
• Experience with multiple LLM providers and routing strategies
• Knowledge of data privacy and compliance (e.g. GDPR-aware AI design)

Project Details
Start: ASAP
Duration: Until 31.10.2026 (extensions possible)
Estimated Effort: 960 hours
Location: Fully remote
Language: English
Candidate Location: EU

We are looking for a Senior Copilot Studio AI Developer to design, build, and optimize AI agents using Microsoft Copilot Studio for enterprise clients.

You will work on intelligent automation solutions where AI agents support internal operations, customer service, document handling, workflow execution, and business productivity improvements.

Responsibilities

• Design and develop AI agents in Copilot Studio
• Configure prompts, conversation flows, logic, actions, and agent behaviors
• Integrate agents with enterprise data sources, APIs, Microsoft ecosystem tools, and workflows
• Build automations using Power Platform components
• Test, monitor, and optimize agent performance and response quality
• Improve usability, reliability, and business efficiency of AI solutions
• Work with stakeholders to translate business needs into AI assistants

Requirements

• 4+ years of commercial experience in software / automation / AI solutions
• Hands-on experience with Microsoft Copilot Studio
• Strong knowledge of Power Platform (Power Automate, Power Apps is a plus)
• Experience with conversational AI / LLM-based systems
• Understanding of prompts, orchestration logic, integrations, and workflows
• Good spoken and written English
• Strong communication skills in client-facing environments

Nice to Have

• Experience with Azure ecosystem
• Knowledge of enterprise automation processes
• Experience integrating CRM / ERP / document systems
• Prompt engineering experience

Conditions

• Duration: 6+ months
• Remote

At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed.

We are looking for a Senior ML & AI Engineer to join us as part of our team.

About the role

PitchBook’s Machine Learning Engineer is responsible for using machine learning, statistical modeling, and natural language processing (NLP) to collect a high volume of data for the PitchBook Platform and surface insights for the capital markets. This role will also serve as a resource and mentor for junior members of the team. A strong motivation to succeed is critical, and everyone has the opportunity to shape the long-term direction of our team.

About the project

PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others. Features of PitchBook: Advanced search / Discovery & insights / Company profiles / Workflow & efficiency / Financials, and many more.

Team

Engineering Manager, 6 ML & AI Engineers

Work Environment

The role offers a flexible work schedule, allowing you to adapt your working hours with the requirement to attend all team meetings. The team follows a Scrum-based Agile methodology.

As a qualified expert, You will

• Work with GenAI & LLMs: Design, develop, and fine-tune generative AI models using transformer architectures. Implement advanced techniques to create practical solutions for real-world challenges.
• Use Modern Toolkits: Leverage frameworks like LangChain, LangGraph, and LangSmith to build automated pipelines and intelligent agents. Integrate these tools with vector databases for efficient similarity search and real-time analysis.
• Extract Data Insights: Process and analyze large volumes of unstructured data using embeddings and statistical modeling to identify meaningful insights.
• Build Scalable Systems: Collaborate with cross-functional teams to create production-grade ML systems that handle real-time, high-volume data processing.
• Mentor & Lead: Act as a mentor for junior team members and stay updated on trends in Agentic AI, transformer models, and generative systems.
• Collaborate & Communicate: Work with Product and Engineering teams to improve data management and support innovation. Present complex analyses clearly and effectively.
• Tech Stack & Focus Areas:
• Work with generative AI and transformer models, integrating frameworks like LangChain, LangGraph, and LangSmith to automate workflows and build intelligent systems.
• Use PyTorch, Scikit-Learn, and Spacy to build NLP pipelines. Implement vector databases for high-dimensional data embeddings and develop hybrid models to extract insights from diverse data sources.

We’re looking for you if you have:

• Educational Background: You hold a Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or a related field.
• Experience: You bring 4+ years of production experience in the ML field, with a track record in developing transformer-based models and deploying them in scalable systems.

• Technical Expertise:

  - Proficient in Python and machine and deep learning frameworks like TensorFlow, PyTorch, ScikitLearn, Spacy, or others.

  - Experienced in natural language processing and modern LLM-based techniques.

  - Experience with full ML lifecycle, data preprocessing, training, evaluation stage, and monitoring techniques.

  - Skilled with SQL and data extraction methods.

• Innovative Thinker: You thrive on solving complex problems and are always exploring new trends in Generative AI.
• Collaborator & Communicator: You’re an excellent communicator able to collaborate and professionally express your thoughts to team members, other engineering teams, and stakeholders.
• Domain Knowledge: Exposure to financial services, investment banking, venture capital, or private equity is nice to have.

What’s in it for You

Reveal great tech solutions

Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling client growth. Unleash your potential, tackle new challenges, and be part of a team that values your skills and contributions. Focus on long-term impact and building tailored, long-lasting partnerships with our clients.

Experience an agile and flexible working environment

Enjoy the freedom of fully remote work with a flexible working schedule. Empower yourself with a stable workload and a stable income, supported by provided laptops and licensed software. We focus on lasting cooperation and unite result-oriented individuals who stand on a high-performance approach to work.

Embrace the opportunity for personal and professional growth

Benefit from performance and merit reviews, elevate your skills with personal development plans, and individual learning through the corporate library, public speaking support, and more.

Be among like-minded people

Work with a team of one mind who cares about what they do and how they do. Collaborate with top-notch experts who are always ready to help and support you through any challenges. Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values. Feel supported by your HR, and take advantage of our referral bonus program.

Interview steps

• Pre-Screening with the recruiter
• Technical interview (up to 90 min)
• System Design interview (up to 45 min)
• Behavioral Round with Product Manager (45 min)
• Final interview with Engineering Manager (60 min).