Jobs ML / AI

Project Overview 

The project involves creating an intelligent chatbot that enables users to query a complex knowledge base (intranet pages, posts, PDFs, etc.). The technical challenge is building a high-performance AI service within the existing .NET ecosystem. The solution must handle sophisticated multi-tenant access control, ensuring the AI generates answers strictly based on the user's dynamic access rights. 

Technical Approach 

• AI Service: Developed as a standalone microservice using .NET 8/9 and C#. 
• Integration: Seamless communication with the existing .NET/Angular platform via REST API. 
• AI Frameworks: Utilization of Microsoft Semantic Kernel or similar .NET-native AI orchestration tools. 

• Core Logic: Implementation of RAG (Retrieval-Augmented Generation) pipelines and vector search within the .NET stack. 

   

Duties and Responsibilities 

• Design and implementation of a .NET-based AI microservice utilizing OpenAI models. 
• Development of RAG pipelines in C# to process and retrieve information from various document formats. 
• Implementation of Tenant-Aware AI logic to mirror the platform’s dynamic role-based access structure. 
• Ensuring high response quality with mandatory Source Attribution (links to original documents). 
• Applying technical guardrails to minimize hallucinations and ensure factual accuracy. 
• Deploying and managing the microservice within a Dockerized environment. 
• Collaborating with the core .NET team to define a scalable integration and data synchronization strategy. 

• Creating technical documentation for the internal development team. 

   

Skills and Qualifications 

• B.Sc. or M.Sc. degree in computer science, applied math, or similar. 
• 7+ years of experience in .NET development (C#), with a strong focus on backend architecture. 
• 2+ years of experience working with Generative AI, LLMs, and OpenAI APIs within the .NET ecosystem. 
• Proficiency in Microsoft Semantic Kernel, AutoGen, or LangChain.net. 
• Solid understanding of vector databases (e.g., Pinecone, Weaviate, or pgvector) and RAG techniques. 
• Experience with Docker and cloud-native deployment (AWS, UpCloud, or Azure). 
• B2+ English communication skills. 
• High responsibility, pro-activity, and attention to technical details. 

We're hiring Low-Level Engineers to design and build RL environments that teach LLMs kernel development, hardware optimization, and systems programming. The goal is to create realistic feedback loops where models learn to write high-performance code across GPU and CPU architectures.

This is a remote contractor role with ≥4 hours overlap to PST and advanced English (C1/C2) required.

Requirements

Minimal Qualifications

• Strong Python (engineering-quality, not notebook-only)
• Production mindset (debugging, reliability, iteration speed)
• Clear understanding of LLMs, their current limitations
• Ability to meet throughput expectations and respond quickly to feedback

You may be a good fit if one of the following applies

• Deep understanding of memory hierarchies (registers, L1/L2/shared memory, HBM, system RAM) and their performance implications
• Threading models, synchronization primitives, and concurrent programming (warps, thread blocks, barriers, atomics)
• Cache coherence, memory access patterns, coalescing, and bank conflicts
• JIT compilation frameworks (e.g., Triton, JAX/XLA, TorchInductor, Numba)
• AOT compilation and optimization passes (LLVM, MLIR, TVM)
• Compiler and kernel frameworks such as CUTLASS, BitBLAS, or JAX/Pallas
• Modern C++, including templates, concurrency, and build systems
• Assembly-level programming and low-level optimization across GPU and CPU architectures (e.g., x86, ARM, NVIDIA Hopper, NVIDIA Blackwell)
• Debugging and optimizing GPU kernels using CUDA and/or HIP/ROCm
• Developing PyTorch custom operators, backend extensions, or dispatcher integrations (e.g., ATen, TorchScript, or custom backends)
• Customizing, extending, or optimizing c, including distributed inference workflows
• GPU communication libraries and collectives, such as NVIDIA NCCL, AMD RCCL, MPI, or UCX
• Mixed-precision and low-precision kernels (e.g., FP16, BF16, FP8, INT8), including numerical stability and performance trade-offs

We’re hiring RL Environments Engineers to design and build MLE/SWE environments that deliver high-quality, diverse tasks with minimal supervision. You will target a specific language model, meet a defined difficulty distribution, and deliver about one task every 10 hours. This is a remote contractor role with ≥4 hours overlap to PST and advanced English (C1/C2) required.

Responsibilities

• Design and build MLE/SWE environments and diverse tasks.
• Target a specified language model and satisfy the required difficulty distribution.
• Deliver ~1 task per 8-10 hours once onboarded.
• Edit tasks within 24 hours based on customer feedback.
• Onboard quickly and start delivering on day one with minimal supervision.
  
   

Requirements

What we’re looking for (must-haves)

• Strong Python (engineering-quality, not notebook-only).
• Hands-on LLM/GenAI work in production: you’ve shipped and operated real systems (not “wrapped an API and called it AI”).
• Strong product/engineering ownership: comfortable building, fixing, and scaling end-to-end pipelines.
• Docker + production mindset (debugging, reliability, iteration speed).
• ≥4 hours PST overlap and advanced English (C1/C2) for specs, reviews, and feedback.
• Ability to meet throughput expectations and respond quickly to feedback.
   

Strong signals (nice-to-have, big plus)

• Experience designing environments/tasks for RL and/or evaluations.
• Experience in high-stakes or regulated domains (e.g., healthcare, finance, fraud/risk, safety-critical systems).
• ML systems experience: CI/CD, monitoring, evaluation harnesses, MLOps, scalable pipelines.
• Systems depth: C++/Rust/Scala/Java, performance/infra optimization, distributed systems.
• Exposure to RL / bandits / agentic systems (not required, but a strong signal).
   

Not a fit if

• You’re primarily a prompt engineer without strong ML/engineering foundations.
• You’re a research-only / academic-only profile with little or no shipping/production ownership.
• You’ve only built in notebooks or rely heavily on managed AutoML tools.
  
   

Working Conditions

• Remote, independent contractor engagement.
   
• 40 hours/week - full time - need 4 hours overlap in the working hours with the team in Pacific time zone;
• Deliverables-driven; begin shipping on day one.

Conversion & relocation: Potential path to FTE and relocation to the Bay Area if performance and mutual fit align.
 

Project Overview:

We are looking for a AI engineer with strong LLM expertise to drive AI initiatives end-to-end — from architecture and implementation to direct collaboration with clients.

Customer Overview:

AI-native financial wellness platform designed specifically for professional athletes, helping them make smarter financial decisions throughout their careers and long after retirement. The platform combines advanced analytics, personalized AI-driven insights, and intuitive financial planning tools to support athletes in managing income, investments, expenses, long-term savings, and financial risks.
 

Responsibilities:

Lead LLM-based solutions from discovery to production

Act as a client-facing engineer:

• Participate in discovery calls & workshops
• Translate business needs into technical solutions
• Explain AI trade-offs, costs, risks, and scalability

Collaborate closely with Sales, Presales, and Delivery

Ensure security, scalability, and cost efficiency of AI solutions

Skills/Requirements:

5+ years in AI

Hands-on experience with LLMs in production

Strong knowledge of:

• LLM APIs & platforms (OpenAI, Azure, AWS Bedrock, Python etc.)
• Self-hosted LLMs & inference optimization
• RAG, vector DBs (Pinecone, Weaviate, FAISS, Milvus)

Experience with model fine-tuning and deployment

• Experience working directly with clients
• Strong communication & presentation skills
• Ability to work in presales / solution design contexts
   

Quantum is a global technology partner delivering high-end software products that address real-world problems.

We advance emerging technologies for outside-the-box solutions. We focus on Machine Learning, Computer Vision, Deep Learning, GIS, MLOps, Blockchain, and more.

Here at Quantum, we are dedicated to creating state-of-art solutions that effectively address the pressing issues faced by businesses and the world. To date, our team of exceptional people has already helped many organizations globally attain technological leadership.

We constantly discover new ways to solve never-ending business challenges by adopting new technologies, even when there isn’t yet a best practice. If you share our passion for problem-solving and making an impact, join us and enjoy getting to know our wealth of experience!

About the position

Quantum is expanding the team and has brilliant opportunities for a Senior Data Science / LLM Engineer. The client is a technological research company that utilizes proprietary AI-based analysis and language models to provide comprehensive insights into global stocks in all languages. Our mission is to bridge the knowledge gap in the investment world and empower investors of all types to become “super-investors.”

Through our generative AI technology implemented into brokerage platforms and other financial institutions’ infrastructures, we offer instant fundamental analyses of global stocks alongside bespoke investment strategies, enabling informed investment decisions for millions of investors worldwide.

Must have skills:

• 3+ years of commercial experience as a Data Science Engineer
• Strong knowledge of linear algebra, calculus, statistics, and probability theory
• Knowledge and experience with algorithms and data structures
• 2+ years of experience with LLM and Natural Language Processing-related tasks
• Knowledge of modern DL architectures
• Experience with at least one of the Deep Learning frameworks (Tensorflow, PyTorch)
• Experience with SQL
• Strong knowledge of OOP
• At least an Upper-Intermediate level of English (spoken and written)

Nice to have skills:

• Experience with production ML/DL frameworks (OpenVino, TensorRT, etc.)
• Docker practical experience
• Experience with Cloud Computing Platforms (AWS, GCloud, Azure)
• Participation in Kaggle competitions

Your tasks will include:

• Full-cycle data science projects
• Data analysis and data preparation
• Development of NLP solution and AI-based chatbots
• Developing models and deploying them to production
• Sometimes, this will require the ability to implement methods from scientific papers and apply them to new domains

We offer:

• Delivering high-end software projects that address real-world problems
• Surrounding experts who are ready to move forward professionally
• Professional growth plan and team leader support
• Taking ownership of R&D and socially significant projects
• Participation in worldwide tech conferences and competitions
• Taking part in regular educational activities
• Being a part of a multicultural company with a fun and lighthearted atmosphere
• Working from anywhere with flexible working hours
• Paid vacation and sick leave days

Join Quantum and take a step toward your data-driven future.

About Us 

ChatRevenue.ai was founded by Ratmir Timashev (founder of Veeam) and Vlad Voskresensky (founder of Revenue Grid) to create the next generation of CRM systems. 

We’re building an AI-native CRM platform that completely redefines how sales automation works. 
By structuring data for intelligent automation, we enable agentic AI to design workflows, assist Sales Leaders and Reps in real time, and generate predictable sales funnels – all through a simple, chat-driven interface. 

Our mission: to build a CRM that works for User, not the other way around. 

About the Role 

This role is for engineers who thrive in dynamic environments, where processes are still being shaped and impact is immediate. 

At ChatRevenue, we focus on goals rather than ceremonies – no endless grooming sessions or heavy planning rituals. Decisions are made quickly through open dialogue and collaboration, not through layers of approvals. 

Previous startup experience is not required – what matters is curiosity, independence, and drive. As part of the core team, you’ll have the chance to own technical domains, lead innovation, and grow together with the company. 

What You’ll Do 

• Design, deploy, and maintain GenAI/RAG pipelines for production 
• Integrate LLMs and agentic assistants into business flows 
• Source, ingest, clean, and enrich external data streams 
• Build and manage vector search, embedding stores, and knowledge graphs 
• Explore and implement new ML/GenAI frameworks and multi-agent systems 
• Mentor developers and share knowledge across the team 

What We’re Looking For 

• Strong backend skills: Python, FastAPI, Webhooks, Docker, Kubernetes, Git, CI/CD 
• Hands-on experience with LLMs (OpenAI-family), LangChain/LangGraph/LangSmith, prompt engineering, RAG, vector stores (Azure AI Search, Pinecone, Neo4j, FAISS) 
• SQL, Pandas, Graph DBs (Neo4j), NetworkX, ETL/data cleaning, Kafka/Azure EventHub 
• Proven experience running RAG pipelines in production 
• Familiarity with graph algorithms (community detection, similarity, centrality) 
• Good English for documentation, API design, and teamwork 
• Startup drive, proactivity, independence 
• Willingness to relocate or travel within Europe; full-time availability 

Nice to have 

• Multi-agent LLM frameworks 
• Big Data pipelines in cloud (Azure preferred) 
• Production-grade ML/NLP, Graph ML 

Why Join Us 

• Remote-first culture – work from anywhere 
• Flexible schedule aligned with European time zone 
• Direct impact – your work is visible in the product from day one 
• Professional growth – own technical domains, lead innovation, scale with the company 
• Competitive compensation: $6000-9000 
• Core team – become one of the people who know the product best and shape its future 

If you want to be part of the core team building an AI-native CRM from scratch, we’d love to hear your story. 

We're looking for a talented ML Engineer with a strong background in Natural Language Processing, Large Language Models (LLMs), and fine-tuning on client-specific data.

This is an opportunity to work on cutting-edge AI solutions that combine real-world client needs with custom-tuned large language models.

Requirements: 

Experience with LLM fine-tuning and prompt engineering

Hands-on skills in PyTorch and/or TensorFlow

Experience working with LangChain and NLP model chaining/composition

Ability to preprocess and normalize raw text data effectively

Familiarity with deploying models in real-world applications

Solid understanding of vector stores, embeddings, and inference optimizations

Responsibility: 

Fine-tune and adapt LLMs (GPT, LLaMA, etc.) on domain-specific datasets
Prepare, clean, normalize and structure client-provided text data for training

Build scalable, production-ready NLP pipelines using PyTorch and/or TensorFlow

Work with frameworks like LangChain, Transformers, Hugging Face, etc.
Select and integrate appropriate tools and services for model training and deployment

Collaborate with cross-functional teams to deliver intelligent AI-driven features
 

We are looking for an ML Engineer to join Intelliarts and contribute to an innovative EV charging management platform that powers large-scale charging networks worldwide.

The platform processes real-time telemetry and charging session logs from thousands of stations, creating a comprehensive data foundation for intelligent monitoring and predictive capabilities. As the ML components are just beginning their journey to production, you'll play a key role in designing and implementing the first generation of machine learning solutions focused on anomaly detection, predictive maintenance, and behavioral pattern recognition.

Your work will enable charging network operators to proactively identify equipment issues, predict potential failures before they occur, and maintain optimal service quality — transforming raw charging data into actionable intelligence that keeps the infrastructure running smoothly.

Intelliarts Ltd. is a boutique software engineering company established in 1999, in Lviv, Ukraine. We launch and support dedicated development teams that help startups and technology companies worldwide to build awesome products. With a flat structure, we don’t offer traditional vertical career growth. Instead, we created a working environment encouraging professional and personal growth: challenging projects, tight integration with teams abroad, and learning from experienced colleagues.

Responsibilities

• Translate business/optimization needs into ML problems
• Design and own end-to-end data/feature pipelines
• Build, validate, and productionize models for forecasting, classification, and anomaly detection on operational data
• Define evaluation metrics and experimentation loops (offline simulation, A/B tests, post-deployment monitoring)
• Integrate models into microservices and optimization workflows (batch/real-time inference, APIs, containers)
• Establish MLOps foundations: versioning, CI/CD, monitoring, and drift detection.

• Collaborate closely with domain/optimization engineers and stakeholders to ensure models drive measurable impact

   

Personal Profile Overview

• Comfortable owning the full ML lifecycle (problem framing, data, model, deploy, observe)
• Degree in Data Science, Computer Science, Software Engineering, or related field
• Stability in previous employment history, with a tendency to remain with employers for extended periods
• Experience in managing diverse project activities (not just coding, but also requirements analysis, preparing estimations)
• Clear and effective communication skills, both verbal and written, and the ability to convey ideas, information, and messages accurately and efficiently
• Proficiency in fostering effective collaboration and teamwork activities
• Ability to analyze information, assess situations, and make decisions based on sound reasoning and logical evaluation
• Focus on delivering exceptional customer experiences and prioritizing customer satisfaction
• Analytical thinking, problem-solving abilities, and a strategic approach to technical challenges
• Transparency in sharing information within a team and company

• Willingness to acquire new knowledge and insights to enhance professional growth and performance

   

Required Skills

• 3+ years of hands-on experience in applied machine learning, with a strong focus on classical ML algorithms and structured data
• Experience with Time Series (forecasting, demand prediction) models like ARIMA, Facebook Prophet, LSTM, Gradient Boosting for Time series.
• Deep understanding of unsupervised learning (clustering, anomaly detection). Experience with unsupervised anomaly detection (Isolation Forest, Autoencoders, Gaussian Mixture Models).
• Experience handling high-volume datasets using Big Data technologies like Apache Spark
• Proven track record in MLOps: building end-to-end ML pipelines, deploying models to production, and managing model retraining lifecycles
• Familiarity with cloud-based ML infrastructure (AWS/GCP/Azure)
• Strong expertise in feature engineering for telemetry data (handling irregular timestamps, resampling, missing values)
• Proficiency in error analysis and debugging ML models, including use of metrics like MAE, RMSE, F1, ROC-AUC, and calibration curves

• English Upper-Intermediate (B2)

   

As A Plus

• Experience with Semi-supervised learning and Self-supervised learning for time-series
• Experience with the energy/EV charging domain
• Survival analysis (Weibull models, Cox models)
• Remaining Useful Life (RUL) estimation

• Experience with Kafka / Kinesis / PubSub and stream processing (Flink, Spark Streaming or similar)

   

We Offer

• Fuel your professional growth with paid online courses, conferences, certifications, English classes, a corporate library, and a leadership program
• Thrive in a culture of trust and cooperation with no time trackers and minimal bureaucracy
• Enjoy 20 business days of paid vacation, plus state holidays to prioritize your well-being
• Experience an open-door culture, transparent communication, and top management at a handshake distance
• Enjoy comfortable office vibes with no open space policy, relaxing sports areas, a spacious bar/kitchen, and more
• Achieve balance with our hybrid/fully remote work model
• Receive fair and competitive compensation
• Fuel your productivity and foster a sense of community with complimentary daily lunches
• Participate in meaningful initiatives supporting Ukraine’s victory
• Take flexible sick leave without burdensome documentation and access parental benefits
• Choose from comprehensive medical insurance or a sports compensation package
• Have fun with regular team-building activities, corporate events and celebrations, and unique initiatives like Week in Lviv

Job Description

• 8+ years of experience as an AI/ML Engineer or in a similar data-intensive role
• Hands-on expertise in AI/ML pipelines and model lifecycle management
• Strong experience in data curation, feature engineering, and preparing datasets for ML
• Proven track record integrating ML solutions into CI/CD pipelines
• Experience training, fine-tuning, evaluating, and deploying ML models in production environments
• Proficiency in Python for ML integration and automation
• Knowledge of Jira or other issue tracking systems
• Upper-intermediate English proficiency (spoken and written)

Job Responsibilities

• Lead AI-driven initiatives to integrate smart automation into CI/CD pipelines, accelerating development and reducing manual overhead
• Curate, annotate, and optimize datasets to train models that improve engineering processes
• Build recommendation systems and fine-tune models to enhance team decision-making and workflow efficiency
• Implement AI-based Jira issue triaging to streamline task management
• Collaborate closely with developers and product teams to embed ML solutions that deliver measurable impact

Department/Project Description

We are seeking a highly skilled AI/ML Engineer to lead the digital transformation of our software development and testing workflows.

Our client is a leading American multinational information technology corporation, headquartered in California, USA, recognized as one of the world's largest companies in the IT sector. 

The company specializes in developing and manufacturing computing, data storage, and networking hardware, alongside designing software and delivering services. Their major product lines include personal computing devices, enterprise servers, and a diverse range of printers and imaging products.

The project involves the development of a comprehensive software package for a large OEM vendor. This software is designed for both enterprise and consumer environments and is installed on endpoint devices. It integrates various cutting-edge services, such as AI companion tools, workplace management solutions, and Smart Office integration.

As part of the GlobalLogic team, you will grow, be challenged, and expand your skill set working alongside highly experienced and talented people.

If this sounds like an exciting opportunity for you, send over your CV!

DeepX is looking for LLM engineer (Middle).

Core Requirements (Must-Haves):

• Mandatory Commercial Experience:
  • If you have a background as a developer (e.g., Python Backend, MLOps, Machine Learning, NLP), you must have at least 1 year of dedicated commercial experience with LLMs.
  • If you started your career directly with LLMs, you must have 1.5-2+ years of commercial experience.
• Commercial RAG Application: Proven experience implementing RAG (Retrieval-Augmented Generation) solutions in a commercial environment. You should be prepared to discuss in detail which RAG approaches you have used on your projects.
• Deep Technical Knowledge: A fundamental understanding of, and readiness for, a deep technical discussion on topics such as retrieval strategies, data chunking, embeddings, and vector databases.
• Advanced Prompt Engineering: Strong, practical skills in prompt engineering and context engineering, with the ability to discuss your techniques and their impact in detail.
   

Nice-to-Have:

• Experience working with local or open-source LLMs (e.g., Llama, Mistral).
• Hands-on experience with fine-tuning local LLM models.
• Experience in creating MCP servers.
• Understanding of the A2A (Agent-to-Agent) protocol.
  
  Additional skills:  Qwen, Mistral, Phi, BERT, Transformers, Ollama, vLLM, LoRA,  LLM, NLP, OpenAI, GPT, Anthropic, Claude, Gemini, Gemma, Llama, Mistral, Deepseek

We’re building an intelligent AI agent that doesn’t just answer questions, but reasons, retrieves, understands context, and acts across documents, SQL data, and visual inputs.

If you're excited by rapid experimentation, real users, and pushing LLMs beyond chat - this role is for you.

     What You'll Do

• Design and implement an intelligent chatbot/agent capable of reasoning over documents, structured SQL data, and multimodal inputs
• Fine-tune or integrate LLMs (OpenAI, Ollama, Deepseek, etc.) for contextual Q&A
• Develop text analytics pipelines (sentiment analysis, classification, topic modeling)
• Design image processing systems for tagging, feature extraction
• Experiment rapidly with new ideas, validate hypotheses
• Support and work with Hadoop clusters for big data processing.
  
  What We're Looking For
• 2+ years of experience as ML/AI engineering or in a similar role
• Bachelor’s degree in Computer Science, or a related field
• Strong proficiency in Python, and solid experience with ML frameworks
• Deep understanding of NLP and CV fundamentals
• Hands-on experience with AI/ML projects deployed in production
• Experience integrating LLMs, retrieval pipelines in multi-step reasoning agents
• Practical experience in text/image processing
  
  Nice to Have
• Knowledge of OCR, captioning
• Experience with prompt engineering
• Experience in predictive modeling

We are looking for a Junior+ AI/ML Engineer to join our team at LITSLINK and work on building and integrating machine learning solutions into real production systems.

This role is perfect for someone who already has initial hands-on experience with ML and wants to grow in real product environments with real data, models in production, and close collaboration with backend teams.

Location — Remote
Experience Level — Middle / Middle+

 

Qualifications

. Commercial or strong project-based experience in Machine Learning
. Strong Python skills for data processing and ML development
. Experience with ML frameworks (scikit-learn, PyTorch or TensorFlow)
. Understanding of core ML concepts (supervised learning, model evaluation, overfitting)
. Experience with data preprocessing (Pandas, NumPy)
. Basic understanding of ML model deployment in production environments
. Upper-Intermediate English or higher

Key Responsibilities

. Develop and integrate ML models into production systems
. Work with data preprocessing, feature engineering, and model training
. Build and support basic ML pipelines and MLOps processes
. Collaborate with backend engineers on ML integration via APIs
. Participate in experiments, model evaluation, and performance monitoring
. Contribute to improving ML code quality and best practices

What We Offer

. Long-term, full-time cooperation on international projects
. Remote-first format with flexible working hours
. Opportunity to grow into Middle AI/ML Engineer role
. Work with real production systems and real users
. Mentorship from senior ML and backend engineers
. Professional growth in applied AI and MLOps direction
. Stable workload and long-term cooperation with LITSLINK

What happens after you apply

. Quick screening call with our recruiter
. Technical interview with ML / engineering team
. Cooperation offer

Client

Our client is a leading legal recruiting company aiming to build a data-driven platform specifically designed for lawyers and law firms. The platform brings everything together in one place — news and analytics, real-time deal and case tracking from multiple sources, firm and lawyer profiles enriched with cross-linked insights, rankings, and more.

Project overview

The platform aggregates data from hundreds of public sources, including law firm websites, deal announcements, legal databases, and media publications, creating a unified ecosystem of structured and interconnected legal data.

It combines AI-driven enrichment, automated data processing, and scalable infrastructure to ensure comprehensive and reliable coverage of the legal market.

Position overview

We are looking for a Lead AI/ML Engineer who will also take ownership of AI architecture, technical strategy, and team mentorship.

Responsibilities

• Develop and deploy AI/ML models leveraging GenAI (OpenAI API) for natural language understanding, summarization, and insights extraction
• Build named entity recognition (NER) and entity linking solutions tailored to legal domain data (law firms, cases, deals, people)
• Implement scalable NLP pipelines for processing news, legal documents, and transaction data from multiple sources
• Design, train, and evaluate ML models to improve search, classification, and recommendation features
• Collaborate with AWS teams to deploy and maintain models using SageMaker, manage datasets on S3, and ensure reliable operation and scalability
• Integrate AI capabilities seamlessly into the web platform, working alongside front-end and backend engineers
• Continuously research new AI models and NLP techniques relevant to legal data and user experience

Requirements

• Strong experience designing and implementing end-to-end AI/ML solutions in production environments.
• Hands-on experience with GenAI, including practical usage of the OpenAI API.
• Solid background in NLP, with hands-on experience in Named Entity Recognition (NER) and entity linking.
• Hands-on experience working with AWS, including services such as SageMaker and S3.
• Strong Python skills with practical experience using TensorFlow, PyTorch, and Hugging Face Transformers.
• Practical experience designing and implementing RAG (Retrieval-Augmented Generation) solutions.
• Proven experience building and implementing Recommendation Engines.

Nice to have

• Experience architecting scalable AI/ML systems on Amazon Web Services.
• Experience working with complex, data-intensive machine learning pipelines.
• Experience combining NLP, GenAI, and recommendation systems within a single solution.

Вітаю тебе, фахівець \0/

Мене звати Кирил, я допомагаю зібрати дрім тім для своїх друзів з Hooh – це стартап від фаундерів Depositphotos. Команда сіньорна та досвідчена, тому опортʼюніті, ІМХО, дуже кльове.

отже

Hooh – це b2c AI-продукт, який робить складні документи простими для розуміння та дій. 

Він дозволяє завантажувати файли (PDF, фото, скани), автоматично розпізнавати їхній вміст, витягувати ключову інформацію та отримувати короткі, зрозумілі підсумки.
 

Ми успішно випустили v1, підтвердивши нашу основну AI-пайплайн-архітектуру. Зараз ми розширюємо продукт до “scanner-grade” PDF-досвіду, що поєднує глибоке AI-розуміння з надійним редагуванням документів.

Далі дозволю собі перемкнутись на англійську, бо мова піде про конкретні речі.
 

_________________

CEO about the role: “We are looking for an Applied AI Engineer who will help us make messy documents reliably understandable, build accurate structure and entity extraction, strong retrieval, and cost‑efficient model orchestration."

Key Responsibilities

• Document analysis & taxonomy: Systematically analyze layouts/sections/entities/relations; define categories, types, and metadata schemas that generalize across domains.
• Prompting & schemas: Author, version, and maintain prompts/chains for classification, extraction, summarization, and Q&A; enforce JSON/JSON‑Schema outputs.
• Retrieval (RAG): Build hybrid retrieval (embeddings + keyword + graph traversal); manage embedding generation, indexing, dedupe, and drift.
• Cascades & MoE: Architect model cascades (fast filters → experts → fallback LLMs) and MoE routing to balance quality/cost/latency.
• Agentic orchestration: Use CrewAI (or similar) to coordinate multi‑step pipelines; add caching/batching/streaming and robust retries.
• Result fusion: Combine outputs via reranking, voting, and confidence/consistency checks; add guardrails and safety rules.
• Evaluation & monitoring: Run LangSmith/Evals (or similar), define golden sets, automate regressions, track quality/cost/latency; canary and observe.
• Operate models locally/hosted: Work with vLLM/TGI/Ollama/llama.cpp as well as API models; apply quantization/LoRA when useful.
• Data stores: Use vector DBs plus graph and relational DBs to power retrieval and joins (e.g., Qdrant/Weaviate/Milvus/pgvector + Neo4j + PostgreSQL).

Required Skills & Experience

• Strong programming skills in Python; ability to write clear, tested, maintainable code.
• Experience building at least one LLM‑powered feature end‑to‑end (prototype → production), or equivalent open‑source/portfolio work.
• Hands‑on with RAG, vector embeddings, and evaluation (offline + online A/B, error analysis).
• Familiarity with model cascading/MoE concepts (or ability to learn quickly).
• Practical database skills: comfortable with SQL and with either graph or vector systems.
• Product mindset: bias to measure impact and iterate.

We hire for capability and learning speed. If you’re strong on fundamentals and can show work, we’d love to talk, even if you don’t check every box.
 

Nice to have:

• OCR/Document AI, ontology design, labeling workflows.
• LangSmith/Evals, LangChain/LlamaIndex; Ray/Airflow; GPU basics.
• Security & privacy for AI systems (PII handling, GDPR), prompt/response guardrails.

The Team

• CEO
• Product Manager
• Fullstack Engineering Lead
• Back End Dev
• Fullstack Dev 
• AI engineer
• User Acquisition Manager
• 2х Designers 
• 2x iOS Devs
  
   

What We Offer

• Challenging technical problems and the environment to solve them.
• Flexible schedule and fully remote work from anywhere with reliable overlap to CET.
• 18 paid vacation days, 8 no-paperwork sick days, and 11 public holidays per year.
• Minimal bureaucracy, no micromanagement, and swift decision-making.
• Direct influence on strategy and the ability to see your experiments ship fast.
• Be part of a team that previously built and scaled successful businesses.

Hiring Process:

1. Intro Call
2. Interview with CEO
3. Offer
    

FluidLabs is building production-grade AI systems and is looking for an engineer experienced in designing and operating LLM-based architectures.

The role requires practical experience with Claude (Opus / Sonnet), prompt architecture, context management strategies, and structured LLM workflows integrated into backend services.

Key responsibilities:

• Design and implement production-grade LLM workflows using Claude (Opus / Sonnet)
• Architect structured prompt systems (system/user/tool separation, multi-step flows)
• Design context management strategies (long-context handling, memory layering)
• Reduce hallucinations through prompt strategy, evaluation loops, and guardrails
• Integrate Claude via API into backend services (Node.js / TypeScript or Python)
• Build RAG pipelines (embeddings, vector search, context injection)
• Design LLM-driven automation (agents, tool calling, workflow orchestration)
• Evaluate and improve response quality through structured testing
• Optimize performance, latency, and cost efficiency
• Collaborate with backend and architecture teams to embed AI into real systems
   

Must-have technical skills:

• 4–5 years of backend engineering experience (Node.js or Python)
• Hands-on production experience with Claude (Anthropic API)
• Strong understanding of:
  • Prompt architecture
  • Context construction and management
  • Multi-step reasoning flows
  • LLM limitations and hallucination patterns
     
• Experience integrating LLM APIs into backend systems
• Understanding of RAG concepts and embeddings
• Experience working with structured JSON outputs and tool-calling patterns
• Ability to explain architectural trade-offs clearly
   

Nice to have:

• Experience building AI agents or autonomous workflows
• Familiarity with vector databases (pgvector, Pinecone, Weaviate)
• Experience with AWS (Lambda, API Gateway)
• Experience in multi-tenant SaaS systems
• Understanding of AI safety and PII handling

What we offer:

• Opportunity to shape a new DocuSign-focused platform from the ground up
• High level of technical ownership and architectural influence
• Enterprise-level projects with real architectural and scalability challenges
• Long-term collaboration on meaningful projects
• A passionate team with a healthy, positive culture
• Enterprise-level projects with real technical challenges
• Room to grow — both professionally and personally
• Referral bonuses for successful candidate recommendations
• Paid holidays and time off
   

Work terms:

• Working hours: 11:00–20:00 (Kyiv time)
• Vacation: 24 paid days per year
• Trial period: 3 months