Jobs ML / AI

Description

GlobalLogic has been engaged in exploring opportunities to implement ML/AL/GenAI-powered applications for multinational industrial conglomerate since 2023, aiming to enhance business efficiency.

You will be working on projects implying complex data processing pipelines for predictive maintenance anaylitics.

Projects tech stack: Kubernetes, Terraform, Helm, LLM, ML, AI, Asyncio, Python, Pandas, HayStack, Azure Blob Storage, Azure DevOps, SQL Alchemy, Docker, Docker Compose, PySpark, PostgreSQL, FastAPI

 

Requirements

• 4+ years in backend/performance engineering (Python and/or C++) with production services.
• 1+ year hands-on with GPU-accelerated AI/ML inference (Triton/TensorRT/CUDA) in production.
• Proven delivery of NIM- or Triton-based microservices behind REST/gRPC (autoscaling, rollout strategies, monitoring).
• Practical experience profiling and removing GPU bottlenecks (memory, kernels, launch configs, batching, concurrency).
• Strong system design skills (throughput/latency/SLA) and high code quality (tests, reviews, docs).
•  

Job responsibilities

• Design, build, and operate NIM-powered microservices (LLM, Embeddings, Reranker, ASR/TTS, VLM) for product features.
• Optimize inference end-to-end – TensorRT-LLM engines, quantization, batching, concurrency, KV-cache, CUDA kernels where needed.
• Package & deploy via Triton + Kubernetes (Helm), set up GPU scheduling, MIG/MPS, canary/blue-green strategies.
• Expose stable APIs (REST/gRPC), versioning, auth/rate-limits; deliver SDK/client wrappers for internal teams.
• Instrument – metrics/logs/traces (Prometheus/Grafana/Otel), DCGM, alerts, SLOs, runbooks, cost/capacity planning.
• Collaborate with DS/Platform/Product on priorities, model choices, and integration paths; ship incrementally.
• Maintain quality & security: tests, CI/CD, IaC, dependency hygiene, secrets management, network policies.
• Document designs, benchmarks, and operational playbooks.

Tools

NVIDIA – NIM, Triton, TensorRT / TensorRT-LLM, CUDA, cuDNN, cuBLAS, NCCL, Nsight, DCGM
Backend/AI – Python, FastAPI, AsyncIO, PyTorch/ONNX, vector search/RAG
Platform – Docker, Kubernetes, Terraform, Helm, Azure Blob/DevOps, PostgreSQL, Redis, Kafka, Grafana/Prometheus/Otel

#WeAreIn for jobs that impact everyone's life. What if your ideas could change the way the world connects, powers up, or thinks? As a Senior AI/ML Engineer on our Research & Development team, you'll have the opportunity to merge creativity with your technical expertise by shaping the future of technology, driving groundbreaking projects, and bringing new ideas to life. Are you in?

Key responsibilities in your new role

Work on innovative IoT, Industrial, and Automotive technologies in a fast-moving R&D team, turning ideas into real products and patents. 

• Design and develop efficient Neural Network (NN) models with sophisticated architectures for embedded domain, strongly involving application-specific knowledge
• Plan and execute practical experiments to collect/create high-quality datasets
• Combine NN and classic signal processing approaches to make the highly efficient pipelines of problem solving
• Implement the developed solutions under real-world end-user scenarios and requirements and verify its functionality at the Infineon hardware (PSOC, wireless SOCs)
• Help with ML/AI related design and implementation in different team projects

Your Profile

Qualifications and skills to help you succeed

• A master’s degree in electrical engineering or computer science (or equivalent) and 2-3 years of related research/engineering experience
• Strong background in mathematical modeling and numerical analysis
• Knowledge of modern NN architectures, Machine Learning tools and trends
• Proficiency in Python and at least one ML framework (PyTorch or TensorFlow)
• Experience with at least one of the following: signal / speech /image / video processing / wireless communication is a plus
• Understanding of embedded systems and Knowledge of Nvidia Deep Learning framework, and familiarity with patents, papers, publications on relevant technical subject matter is a plus
• Intermediate English writing, communication, and presentation skills

Please send us your CV in English.

Description
About ProfiSea Labs 
ProfiSea Labs is an innovative AI-powered multi-cloud management platform designed to help CloudOps engineers and IT managers optimize cloud usage, reduce costs, and automate workflows through intelligent, next-generation technologies. 
We’re building a new AI division focused on agentic GenAI systems — autonomous agents capable of reasoning, learning, and interacting with users and systems. We’re looking for curious, ambitious minds to help us design, train, and scale these agents. 

Requirements
 

• English C1+ (strong writing and reading skills). 
• Python knowledge — basic to intermediate. 
• Excellent literacy and attention to detail. 
• Experience with LLMs (e.g. GPT, Gemini, Claude, Copilot). 
• Understanding of Agentic AI design or prior experience building agents is a plus. 
• Analytical mindset and creativity in solving interaction problems. 
• Strong interest in AI technologies and willingness to learn continuously. 
• Students or recent graduates are welcome to apply — the main requirement is your curiosity and drive to grow. 

Responsibilities

 

• Develop, optimize, and test AI prompts and workflows in English. 
• Create dialogues, flows, and interaction scenarios for AI agents. 
• Tune the agent’s behavior across various contexts. 
• Work with test flows, analyze failures, and improve model responses. 
• Collaborate with product managers, designers, and developers to integrate AI into real products. 
• Experiment with LLMs (Large Language Models) and contribute to fine-tuning and configuration processes. 

Test Task 
A short technical and creative task will be required to assess Python proficiency, prompt design ability, and reasoning skills. 

Working conditions
 

• Opportunity to take part in building an innovative AI product. 
• Ongoing learning and mentorship from experienced AI engineers. 
• Flexible schedule and fully remote work. 
• Competitive compensation. 
• Friendly and professional work culture. 
• Freedom to influence product development and bring your own ideas to life. 

 
At ProfiSea Labs, we don’t just use AI — we teach it to think. Join us if you’re ready to do the same.

Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.

5+ years of hands-on experience in AI / Machine Learning development, including production deployments.

Strong proficiency in Python and common AI/ML libraries (PyTorch, TensorFlow, scikit-learn, pandas, NumPy).

Proven experience with time-series analysis and forecasting models (e.g. ARIMA, Prophet, LSTM, GRU).

Experience designing and training models for anomaly detection, prediction, and optimization.

Solid understanding of the full ML lifecycle: data preparation, feature engineering, training, validation, deployment, and monitoring.

Experience deploying AI/ML models to production environments, preferably on cloud platforms (Azure preferred).

Strong data engineering skills: SQL, data pipelines, handling large-scale and streaming datasets.

Familiarity with MLOps practices, CI/CD, model versioning, and monitoring tools (e.g. MLflow).

Experience working with IoT or sensor-based data is an advantage.

Background in energy, utilities, or industrial data analytics is a strong advantage.

Ability to lead AI initiatives end-to-end and take technical ownership.

Strong analytical, problem-solving, and system-design skills.

Excellent communication skills and ability to work with cross-functional teams.

Numerical Technologies, a leading provider of software development and consultancy services for financial institutions, is looking for talented people experienced in Math and Python who will be responsible for development, testing, implementation, debugging and support of sophisticated financial applications.

Joining Numerical Technologies is a unique chance that provides the opportunity to work alongside experienced professionals on impactful, quantitative projects across North and South America, Europe, the Middle East, Africa, CIS and South Asia. This position is a chance to work hand-in-hand with expert quantitative researchers and quantitative engineers to create and implement research and simulation tools that leverage existing automated trading software and sophisticated statistical techniques and technologies.

Do you enjoy the process of problem solving, a process where you recognize areas of improvement and iterate and innovate to improve? Does your curiosity and desire to learn drive you? If this sounds like you, don't miss this opportunity to gain valuable experience in a highly competitive and demanding fintech industry.

What are we looking for?

◼️ Deep understanding of Applied Mathematics: Probability Theory and Statistics, Numerical Methods, Linear Algebra, Optimization

◼️ Bachelors, Masters or Phd in Physics, Mathematics, Statistics; or equivalent experience (we also welcome students)

◼️ At least 0.5 year of commercial experience as a Python Developer

◼️ Practical experience with AI technologies, particularly in applying them to solve real-world business problems
◼️ Basic understanding of NLP concepts
◼️ Familiarity with LLMs, embeddings, vector databases, and RAG architectures

◼️ Willingness to learn together with a team of professionals
◼️ Experience working with LLM APIs (OpenAI, Anthropic, Azure OpenAI, etc.)

◼️ Knowledge with Git, Jira, and Confluence

◼️ Excellent communication skills

◼️ Intermediate and higher level of English.

Please mention in your application any participation or awards in mathematics, physics, or programming Olympiads.

What will you be doing?

◼️ Collaborating with cross-functional teams to develop AI-driven solutions that address specific business challenges.

◼️ Continuously learning and staying updated on the latest AI technologies and best practices, applying this knowledge to improve existing projects.  

What additional skills will help you stand out?

◼️ Experience in Machine Learning

◼️ Participation in Kaggle competitions

◼️ Experience working at a financial institution

◼️ Experience working with cloud platforms (AWS, Azure)

We offer:

◼️ Competitive compensation depending on experience and skills

◼️ Regular assessments and salary reviews

◼️ Compensation for sick lists and regular vacations

◼️ Health insurance

◼️ English classes with certified English teachers

◼️ Flexible working schedule

◼️ Friendly team and enjoyable working environment

◼️ Comfortable, spacious office within 10 min from all three Metro lines.

◼️ Corporate and social events.

Description

About the Role
You will be part of the Engineering team, driving the evolution of Batch AI, our AI-powered system that optimizes material feeding in concrete production. Batch AI combines sensor data with predictive and machine learning models to make batching operations more accurate, consistent, and autonomous.
As a Senior ML Engineer, you will apply machine learning engineering and applied data science techniques to design, train, and evaluate models that directly impact industrial performance,
working closely with engineers and the Product Operations team.
This is a hands-on role for someone who thrives on ownership, experimentation, and delivering real-world impact. You will play a key role in advancing the intelligence and reliability of industrial
processes powered by Batch AI.

Requirements:

Qualifications
Must-Have
 

• BSc or MSc in Data Science, Applied Mathematics, Computer Science, or equivalent practical
  experience.
• 5+ years of hands-on experience in applied machine learning, with a focus on regression,
  forecasting, or optimization.
• Proven experience in production-grade ML pipelines, from experimentation to deployment.
• Strong grasp of data science concepts such as cross-validation, quantile modeling, and
  model safeguard techniques.
• Strong background in Python and data science libraries, with the ability to write clean,
  efficient, and production-ready code.
• Experience using modern development tools and practices, including Git and related
  workflows, code review processes, automated testing, and CI/CD pipelines.
• Solid understanding of data lifecycle management, including time-series feature engineering
  and retraining strategies.
• Experience with ML model monitoring, versioning, and continuous retraining frameworks.
• Familiarity with cloud ML ecosystems (Azure or AWS)

• Comfortable documenting and presenting analytical findings to mixed technical and non-technical audiences.

   

  Nice-to-Have

• Experience with Azure ML, CosmosDB, Service Bus, and Kubernetes.
• Experience with AWS SageMaker, ECS Fargate, SQS/EventBridge, and DocumentDB.
• Exposure to industrial data from manufacturing equipment, batching systems, or other IoT-connected process control environments such as manufacturing, logistics, or energy
  sectors.
• Experience applying software development and engineering best practices, including
  architectural, design, and coding principles.

• Experience with containerized workloads (Docker).
   

   

Job responsibilities

•  Design, train, and validate machine learning models that improve process performance and
  stability in concrete batching operations.
• Lead end-to-end model development, from data exploration and feature engineering to deployment and validation.
• Define and implement retraining and validation strategies that ensure continuous performance improvement.
• Work closely with engineers to ensure models integrate efficiently with production systems in Azure and AWS environments.
• Propose data selection and quality control methods to improve training representativity.
• Contribute to the monitoring and evaluation approach for ongoing model health.
• Document methodologies, experiments, assumptions, and decisions to maintain clarity and
  reproducibility.
• Collaborate with team members, the Product Operations team, and other stakeholders as needed to support large-scale deployment.

A fast-growing SaaS company that is changing the way tax, accounting, and auditing firms work, is looking for an ML Engineer to work remotely.

Founded in 2017, the company now has over 350 employees. The company is trusted by over 10,000 firms in more than 25 countries. Its universal platform helps firms manage documents, payments, workflows, and communications more efficiently.

Current tasks to be worked on:

• Text recognition and classification.

• Text matching.

• Document search.

• Creating a basic level (simple working solution), for example, using prompts via a publicly available API to obtain structured output.

The company's mobile app in the App Store has a high rating of 4.9 out of 5.

What the company offers: 30 days of vacation, paid sick leave, training and development budget.

Requirements: experience with classic NLP and modern LLM, RAG, classification tasks.

Experience with document OCR and model tuning would be a plus.

We are building an AI agent that will ultimately generate a dashboard from financial data. We are looking for an AI Engineer with extensive LLM experience, specifically fine-tuning, to help our finance experts create state-of-the-art dashboard examples we can feed into the agent. We also need to develop a system to evaluate the results and improve them by involving our experts as judges (human-in-the-loop).

What you’ll work on

• Partner with finance experts to define what “great” looks like (KPIs, charts, dashboard layout, insights). 
• Generate synthetic dataset for experts to work with using existing data samples of real companies.
• Fine-tune the LLM with the SOTA dashboards that we will prepare
• Build an evaluation and human-in-the-loop system to improve AI Agent responses.
• Work on AI agent architecture improvements, including prompt updates and prompt engineering.

Nice to have

• Familiarity with finance dashboards, FP&A, accounting, BI

Please include 2 case-studies of LLM fine-tuning or LLM response improvement that you delivered. 

About PICHE

PICHE is Latvia’s leading business park development company with 20 years of experience and over 150 successfully implemented projects in Europe.

We specialize in the development, design, and building of business parks. PICHE is about an improved and organized work environment, so you can focus on what matters most at work.

Ready to lead real change with AI?
Join us and work side by side with one of the company’s top leaders to embed AI into the very core of how we operate — from accounting and procurement to workflow optimization and decision-making. This is a rare opportunity to not only deploy cutting-edge technologies but to shape how an entire organization thinks and works. If you’re passionate about real-world AI applications and want to see your work have a visible business impact — we want to talk to you)

We are currently looking for two specialists to expand our team.

Key Responsibilities:

• Design and implement company-wide strategies for integrating AI into core business operations.
• Partner with various departments to uncover opportunities where AI can streamline and enhance existing processes. 
• Apply machine learning models and smart algorithms customized for specific business use cases.
• Manage the seamless integration of AI tools into the current tech infrastructure. 
• Regularly assess the performance of AI systems to ensure they operate efficiently and reliably. 
• Serve as an internal advisor and mentor on AI topics, helping to build a culture of innovation and AI adoption.
• Stay ahead of industry advancements and embed the latest AI technologies into the company’s strategic plans. 
• Organize and lead internal workshops and training programs to build AI literacy and usage across teams.
   

Your Qualifications:

• Proven experience (1 year+) deploying and integrating AI solutions in real business environments.
• English B2.
• Hands-on experience with automation platforms like n8n, Make, Zapier, or equivalent tools.
• Strong communication skills, with the ability to work effectively across technical and non-technical teams. 

• Solid background in API integration within scalable system architectures.

   

Nice-to-Have Skills:

• Practical experience applying AI/ML in business workflows.
• Experience with automating business processes using AI or large language models (LLMs).
• Proficiency in designing and implementing API integrations and workflow automations. 
• Familiarity with cloud platforms such as AWS, Azure, or GCP for deploying AI solutions.
• Successful involvement in cross-departmental projects combining business and tech teams. 
• Working knowledge of modern LLMs like ChatGPT, Claude, or similar. 
• Background in digital transformation, product development, or innovation-focused roles. 
• Ability to code in Python or other AI-friendly programming languages.
   

We’re happy to provide you with:

• Salary from $1800+tax;
• Fully remote work opportunities;
• Breathtaking challenges, where everything is possible;
• Annual paid leave — 20 working days;
• Paid day off on your Birthday;
• Official national holidays are our days off;
• Сompensation for studies and sports;
• Corporate accountant support.

The Project is unlocking capital with AI by transforming credit origination through an intelligent platform that enables lenders to automate manual bottlenecks, underwrite 5× more loans, and reduce risk.

Founded by a team with deep domain expertise and a strong execution track record, and backed by a successful pre-seed round and strategic partners, we are building foundational AI infrastructure for the $3T+ asset-based finance market.

We are seeking an experienced AI Engineer to build production-grade AI applications that can understand, analyze, and reason over complex financial data and documents. This role goes beyond orchestration and surface-level integration: you will work deeply with LLMs, NLP, document processing pipelines, retrieval systems, and evaluation frameworks to deliver reliable, interpretable credit insights.

Success in this role requires strong fundamentals in NLP, Transformer models, document understanding, and system evaluation, alongside hands-on experience building and operating LLM-powered systems in production.

Key Responsibilities

• Design and build LLM-based systems capable of multi-step analysis across structured data, unstructured documents, and external knowledge sources.
• Implement advanced analytical workflows with chain-of-thought, tree-of-thought, and hybrid reasoning approaches, to improve task accuracy and robustness.
• Build rigorous evaluation frameworks that go beyond simplistic benchmarks like LLM-as-a-judge.
• Architect memory, context management, and workflow orchestration layers to support complex pipelines with optional human-in-the-loop review.
• Design, implement, and evaluate RAG pipelines, including document chunking strategies, embeddings, retrievers, re-ranking, and semantic search.
• Develop context engineering techniques that systematically manage model inputs, structuring retrieved context, tool outputs, and memory to improve reliability and reduce hallucinations.
• Build and maintain document processing pipelines, including OCR integration, layout-aware parsing, table extraction, and normalization of financial and legal documents.
• Develop ETL and data engineering workflows spanning structured, unstructured, vector, and graph data.

• Deploy, monitor, and operate scalable AI services on AWS or Azure, integrating them into CI/CD and observability pipelines.

   

Qualifications

• 5+ years of professional experience in Python or JavaScript.
• Demonstrated experience building and deploying production-grade AI applications using LLMs.
• Strong fundamentals in NLP, including:
  • Text classification, information extraction, and semantic similarity
  • NLP evaluation metrics (precision, recall, F1, confusion matrices)
• Solid conceptual and practical understanding of Transformer architectures, including:
  • Tokenization and context windows
  • Attention mechanisms (at a high level)
  • Model limitations, failure modes, and trade-offs
• Hands-on experience with document processing systems, including PDFs, scanned documents, tables, and semi-structured data.
• Strong experience with semantic search, retrievers, and RAG architectures, including embedding strategies and vector databases.
• Familiarity with modern LLM frameworks and tooling (e.g., LangChain, LangGraph, or similar) and observability tools (LangSmith, Langfuse, Helicone).
• Strong data engineering foundation: ETL pipelines, SQL/NoSQL databases, vector stores, and graph data models.

• Experience deploying and maintaining AI services on AWS or Azure, with CI/CD, containerization, and infrastructure-as-code.

   

Nice to Have

• Experience with model fine-tuning or adaptation techniques (LoRA, QLoRA).
• Familiarity with knowledge graph construction and usage, including GraphRAG or graph-enhanced retrieval pipelines.
• Exposure to advanced workflow orchestration patterns and evaluation tooling.

Job Description:

• Understanding of software and enterprise architecture patterns (Microservices, Event-driven, Cloud-native).
• 10+ years of experience in software engineering, with at least 5 years in a lead architectural or senior SDLC management role.
• Proven experience with Generative AI, LLM integration, and prompt engineering within the development lifecycle.

• Exceptional presentation skills; ability to simplify complex technical concepts for non-technical buyers.

   

Job Responsibilities:

• Deliver high-impact presentations and demonstrations to stakeholders.
• Actively participate in delivery projects to guide teams through AI SDLC transformations. You are not just a consultant; you are an active participant in the project’s success.
• Utilize and refine existing architecture, integrations, security and relevance to AI (how to adopt AI tools into SDLC for efficiency gain) within the existing AI SDLC Framework to optimize developer velocity, code quality, and security.
• Work directly with delivery squads to remove technical blockers and ensure the practical application of AI tools (like Copilots, automated agents, and AI-driven testing).
• Establish KPIs within delivery projects to quantify the impact of AI transformation (e.g., reduction in cycle time, cost savings, quality improvements).
• Transform project results and "lessons learned" into professional case studies, white papers, and internal knowledge-base articles to fuel further sales cycles.

• Feed delivery insights back into the core AI SDLC Framework for constant iteration and improvement.

   

Department/Project Description:

GlobalLogic is driving a company-wide SDLC AI Transformation, embedding artificial intelligence across requirements, design, coding, testing, deployment, and maintenance to enhance productivity, accelerate delivery, and improve quality. By combining deep engineering expertise with applied AI practices, we enable secure and scalable adoption both within our delivery teams and for our clients. This effort emphasizes innovation, governance, and reusable solutions to help organizations achieve faster time-to-market, stronger quality assurance, and more sustainable digital operations.

We are seeking an experienced Artificial Intelligence / Machine Learning Engineer to join our dynamic team. 

Key Responsibilities: 
➔ Develop autonomous AI agents and multi-agent workflows leveraging tool calling and advanced reasoning. 
➔ Build and optimize LLM-based systems, including prompt engineering, fine-tuning, and performance evaluation. 
➔ Design and implement Retrieval-Augmented Generation (RAG) pipelines integrating external knowledge sources. 
➔ Optimize models and agents for production environments with a focus on latency, throughput, and cost. 
➔ Stay current with the evolving LLM/NLP landscape and apply state-of-the-art techniques to real use cases. 

Requirements: 

• A minimum of 2 years of AI-focused experience and 6+ years of overall engineering background
• Strong proficiency in Node.js (TypeScript) and Python
• Practical experience in fine-tuning, deploying, and optimizing LLMs (e.g., OpenAI (GPT models), Meta (Llama), Anthropic (Claude), Google (Gemini), X.ai (Grok))
• Expertise in NLP techniques, including prompt engineering, embeddings, and semantic search
• Experience with RAG techniques, including working with vector databases and retrieval systems such as Pinecone, Weaviate, Faiss, or pgvector
• Experience with LLM orchestration frameworks (LangGraph, LlamaIndex, etc.) and multi-agent architectures
• English B2. 
  
  Nice to have:
• Experience with Promptfoo or other tools for LLM prompt testing, comparison, and regression tracking, as well as familiarity with LangTest for automated evaluation and validation of LLM performance and robustness
• Experience with transformer-based models and frameworks (Hugging Face, PyTorch, TensorFlow).
• Experience in deploying responsible AI, including implementing safety guardrails and mitigating bias
• Publications or contributions to open-source ML projects

We are looking for a Software Architect with a strong focus on AI/ML to design and implement cutting-edge generative AI solutions.

Customer
Our Customer is a Swedish AdTech company that transforms the way publishers and advertisers interact. They provide state-of-the-art self-serve advertising platforms that enable seamless, direct transactions between advertisers and some of the world’s largest publishers. Their mission is to make advertising more accessible, automated, and efficient through innovative technology. Together we serve globally recognized clients such as TripAdvisor, Bloomberg, The Washington Post, Opera, Dow Jones, etc.

Project

The project is a scalable, white-label ad platform used by major global publishers. It integrates AI-driven automation to enhance advertising transactions, targeting efficiency, and personalization. As part of this initiative, you’ll design AI architecture from the ground up, ensuring production-ready, scalable solutions.

Requirements:

• Leadership: Proven experience building and leading centralized Data/AI teams, including data engineering, data science, and MLOps functions.
• Cloud MLOps: 5+ years of experience deploying ML models into production on AWS (SageMaker, MLOps pipelines).
• SaaS AI Deployment: Experience deploying AI-powered features into customer-facing SaaS platforms, from recommender systems to intelligent assistants.
• API Serving: Proven expertise in exposing ML inference via scalable, low-latency REST APIs.
• Data Pipelines: Strong background designing and managing high-volume data pipelines (e.g., Kinesis, Kafka, Spark) for feature engineering.
• Model Governance: Experience implementing model monitoring, drift detection, and XAI frameworks, including establishing an AI governance framework with model inventory, explainability standards, and ethical review processes.
• Statistical Modeling: Advanced degree (MS/PhD) in Computer Science, Statistics, or a quantitative field.
• Strategy: Demonstrated ability to translate AI capabilities into product strategy, driving alignment with Product, Engineering, and Business teams.
• Data Security & Privacy: Deep understanding of data security, PII handling, and privacy-by-design (GDPR, CCPA, SOC2) as applied to ML systems.

• Upper-Intermediate level of English or higher
   

  Preferred Qualifications:

• AWS Certified Machine Learning Specialty.
• NLP & GenAI: Experience with NLP for content analysis (e.g., creative validation) or LLM integration, including LLMOps, GenAI safety, prompt versioning, and evaluation.
• Experience with probabilistic programming or causal inference.
  
  Responsibilities
• AI Strategy & Integration: Design, train, and deploy production-grade ML models (using AWS SageMaker, Bedrock, Comprehend) for predictive analytics, anomaly detection, recommendation engines, and automation.
• MLOps & Model Serving: Own the entire ML model lifecycle, from training and versioning to secure, low-latency deployment (Model Serving) on AWS.
• Data Strategy & Governance: Define and implement a company-wide data strategy, including governance, quality, lineage, and access control.
• Partner with the Domain Expert to formalize AI-driven rules within the BRD.
• AI Governance: Implement robust monitoring for model drift, bias, and performance.
• Explainability (XAI): Develop and expose “explainability” endpoints for critical AI decisions (e.g., campaign rejection) to support governance and the Agentic Economy.
• Collaboration: Work with the Platform Back-End Dev to define data pipelines and API contracts for real-time model inference.

The project is a big enterprise application with a back office and a mobile client. We’re going to extend its functionality with SmartWatch (WatchOS) and CarPlay applications for which we will also use LLM/Gen AI. You will be part of a cross-functional, cross-geo team of developers, test engineers, UI/UX designers, and architects who will create applications from scratch and integrate them with the back-office API.

Responsibilities: 

• Help the team which consists of BA, Designer UI/UX, Architect, Mobile engineers research and find solutions on how to work with a 3rd party LLM
• Help the team use LLM/Gen AI for mobile applications (iOS, Android and smartwatch)
• Provide feedback to development teams on technical, troubleshooting, or operational issues related to LLM/Gen AI
• Implement and integrate retrieval-augmented generation (RAG)and tool-augmented agents
• Engineer and optimize prompts for LLM-based workflows
• Be up-to-date with advancements in LLMs, agent frameworks, and RAG techniques
• Research and propose improvements to model performance, usability, and tuning
• Prototype ideas for intent recognition, query synthesis, analysis, and reasoning
• Collaborate closely with developers and other stakeholders to ensure system alignment with product and business goals

Requirements: 

• 5+ years of experience in Machine Learning, Large Language Models and Generative AI.
• Strong expertise with LLMs (e.g., Gemini, GPT-4, T5, Claude, LLaMA) and generative AI techniques.
• Hands-on experience with ML frameworks such as PyTorch and LangChain, and LangGraph.
• Experience with using prompt engineering techniques to adapt LLMs effectively and/or fine-tuning LLMs using APIs.
• Strong grasp of data structures and data transformation processes.
• Experience serving ML models as API services in production environments.
• Strong knowledge of different types of databases (SQL, noSQL, Vector, etc).

• At least upper-Intermediate English (comprehensive written and verbal communication)

   

Nice-to-Have: 

• Experience developing AI agents with external tool integration.
• Working with AI agents for Mobile applications

• Working during the discovery phase along with BA, UI/UX, Architect, other engineers

   

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.

About the Role

Our client are developing a platform where AI agents are assembled, configured, and deployed by non-technical users — yet under the surface, these agents rely on deep, carefully engineered logic.

This role sits exactly at that intersection.

You will not be building traditional backends or APIs. Instead, you will design and implement the decision-making layer that defines how AI agents think, react, switch states, and interact with external systems during real conversations.

⚠️ Important clarification:
This is not a Python-first engineering position. While general programming knowledge matters, your daily work will revolve around a custom scripting environment, built on top of Jinja2, used to express complex logic, data transformations, and conversation control. If your primary interest is writing application code in standard frameworks, this role will likely feel uncomfortable. If you enjoy mastering unconventional tools to shape complex system behavior, it will feel rewarding.

What You’ll Be Responsible For

• Designing agent behavior logic
  Build advanced conditional flows, branching paths, and state-aware logic that governs how an AI agent behaves across multi-turn conversations.
• Managing conversation state
  Ensure agents correctly track context, user intent, and system state across interruptions, retries, and edge cases.
• Integrating external systems
  Define how agents exchange data with third-party services (e.g. CRMs, calendars, booking tools) by constructing structured payloads and interpreting responses.
• Creating reusable logic components
  Develop configurable modules that can be reused across many agents by non-technical users without breaking consistency or reliability.

• Troubleshooting and performance tuning
  Investigate unexpected agent behavior (loops, dead ends, incorrect decisions) and optimize logic execution for fast response times in voice-based scenarios.

   

What We’re Looking For

• Strong logical and algorithmic thinking — you are comfortable reasoning about conditions, branching, recursion, and state machines
• Deep experience with templating or embedded logic systems (Jinja2 or similar), including non-trivial data manipulation inside templates
• High confidence working with JSON and complex nested data structures
• Willingness to learn and specialize in a non-standard, proprietary scripting language and become an internal expert

• Ability to work fluently in Russian, with professional-level English

   

Nice to Have

• Experience in solution engineering, implementation roles, or technical consulting
• Background with configuration-driven or low-code systems (e.g. workflow builders, automation platforms)

• Familiarity with Python or other general-purpose languages (for reasoning, not for day-to-day coding)

   

Why This Role Is Unusual

You won’t be shipping endpoints or UI features.
You will be shaping how AI agents reason, decide, and act — in systems where small logic mistakes can break an entire conversation.

If you enjoy building order inside complex, dynamic behavior — this role was designed for you.