Jobs ML / AI

Senior AI / LLM Engineer (GenAI Systems)

On-site Only (Preferred).
 

We’re building a next-gen AI infrastructure platform focused on LLMs, intelligent matching, and scalable systems.
 

Looking for a Senior AI Engineer to design and ship real-world AI systems across models, infrastructure, and production.
 

What You’ll Work On

• Build & scale end-to-end AI pipelines
• Develop and deploy LLM-powered systems
• Optimize inference performance
• Design RAG systems, agent workflows, structured outputs
• Improve prompting, NLP, and data quality
 

Tech You’ll Likely Use

(Depth > breadth)
• Core engineering: CI/CD, version control, containers, APIs, databases
• Python + ML frameworks: PyTorch / TensorFlow / Keras
• LLMs / GenAI: RAG, LangChain, vector systems
• Function calling & structured outputs
 

What We’re Looking For

• Production experience with LLM / ML systems
• Strong understanding of transformers & modern AI
• Able to work across model + system + product layers
• Bias toward shipping real systems (not research only)
 

Nice to Have (Not Required)

• NLP / semantic systems experience
• NER, classification, extraction, structured data pipelines
• MLOps & inference: CUDA, vLLM, Triton, DeepSpeed, FSDP
• Fine-tuning: SFT, DPO, dataset curation
• Advanced degree (PhD)
 

What We Want

• Systems thinker (infra + model + product)
• Ships fast, builds real systems
• Comfortable in high-growth, ambiguous environments

What You’ll Get

• On-site team with strong engineering culture
• Free on-site parking
• Daily  lunches, snacks, drinks
• Performance-based bonuses
• Work on real, high-impact U.S. project
• Clear growth path as the product and team scale

Join Innoneers – Shaping the Future of Software Engineering!
 

At Innoneers, we don’t just build software; we engineer innovation. We are a dynamic software development company dedicated to transforming complex business challenges into seamless digital experiences. Specializing in Web, Mobile, and Cloud solutions, we pride ourselves on our technical agility and "Engineering Excellence" mindset.
 

As we scale our operations, we are looking for a passionate Embedded ML Engineer to join our core team. If you thrive in an environment where clean code meets cutting-edge technology (Node.js, React, AWS, Python) and want to make a tangible impact on global projects in E-commerce, Fintech, and Logistics, Innoneers is the place for you.
 

WE WILL TRUST YOU WITH:

• Lead the design and implementation of state-of-the-art machine learning algorithms

  tailored for both real-world hardware and software applications;

• Build and optimize embedded ML models using PyTorch and NumPy, bringing them

  to life in embedded systems with C and Python;

• Analyze, benchmark, and fine-tune embedded ML pipelines to achieve latency,

  memory efficiency, and power optimization;

• Pioneer explainable AI techniques that provide actionable insights into model

  behavior and drive trustworthy solutions for our customers and end-users;

• Expand our ML toolkit by researching and integrating the latest methodologies,

  staying at the forefront of AI innovation;

• Establish scalable, reproducible workflows that set a standard for excellence in

  embedded ML development;

• Partner with a multidisciplinary team of hardware engineers, data scientists, and

  software developers to deliver integrated software and hardware application use

  cases;

• Provide critical algorithm insights to hardware development teams to facilitate

  seamless integration and performance optimization;

• Support the integration of ML models with hardware AI accelerators and

  neuromorphic processors;

• Collaborate with internal teams and clients to identify and design requirements for

  complex customer use cases;

• Translate customer needs into technical specifications and actionable project plans;
• Present findings and insights to stakeholders to inform decision-making and strategy;

• Mentor junior engineers and interns, fostering a culture of continuous learning and

  technical excellence within the team;

• Contribute to the creation of intellectual property, including patents and proprietary

  know-how.

   

YOUR EXPERIENCE INCLUDES:

• A BSc or MSc in a subject related to ML/Data Science/Statistics or relevant industry

  experience;

• Solid understanding of architecture specifications, and the ability to design and

  develop industry-standard embedded software and methodologies;

• 5+ years of hands-on experience in the development and deployment of AI solutions;
• Previous experience in Embedded or TinyML AI application development;

• Proficiency in C/C++ for logical programming for embedded software development

  and Python for ML model development;

• Strong experience with TensorFlow/PyTorch and Scikit-learn;
• Familiarity with hardware AI accelerator architectures and neuromorphic processors;
• Experience working with time series data/sensor data/real-time data processing;

• Proficiency in using version control systems.

   

WHAT YOU NEED TO SUCCEED:

• Strong analytical and problem-solving abilities with great attention to detail;

• Excellent communication skills for effective collaboration with cross-functional team

  and stakeholders;

• A proactive and self-motivated attitude toward learning and innovation.

   

WE OFFER YOU:

• A dynamic working environment around a fearless engineering culture;
• Ambitious teams with the freedom to innovate;

• A Flexible working environment (work from home policy, flexible working hours,

  advantageous holidays scheme ..);

• An inclusive company culture that embraces communication, diversity, and support

  around holistic and personal development.

Our customer is a leading provider of product lifecycle management and digital thread solutions. Its technology enables the rapid delivery of flexible solutions built on a powerful digital thread backbone and a low-code development platform. You will be involved in building full scale AI-agentic platform from scratch.

Requirements:

• Strong proficiency in Python, experience with web frameworks like FastAPI or similar
• Understanding of the AI application development lifecycle
• Experience with rapid UI prototyping using Streamlit, Gradio, or similar frameworks
• Familiarity with major LLM platforms and APIs (OpenAI, Anthropic, Amazon Bedrock, Gemini) and related frameworks (LangGraph, LlamaIndex, Strands Agents, etc.)
• Knowledge of advanced AI integration patterns (e.g., RAG, Agents)
• Experience deploying AI solutions at scale, with considerations for performance, cost-efficiency, and maintainability
• Proven ability to evaluate generative AI quality using metrics such as retrieval and classification scores, as well as LLM-based evaluation methods
• Proven experience in AI engineering and delivering ML-based solutions
• Strong problem-solving skills and attention to detail
• Excellent communication, collaboration, and interpersonal skills
• At least Upper Intermediate English level

Responsibilities:

• Design, implement, and maintain end-to-end AI applications, including chatbots, Q&A platforms, and agent workflows
• Collaborate directly with clients to understand their needs, identify opportunities, and recommend LLM-driven solutions
• Develop and manage robust data pipelines, prompt strategies, and datasets to ensure effective and accurate AI models (Will be +)
• Evaluate and refine AI system performance, ensuring outputs are accurate, secure, scalable, and compliant with industry regulations (Will be +)
• Conduct research and rapid prototyping to validate technical feasibility and demonstrate business value of AI solutions
• Stay current with evolving LLM technologies, frameworks, and methodologies to continuously improve solutions and client outcomes

What we offer:

• Annual paid vacation of 18 working days.
• Extra vacation days for long-lasting cooperation.
• Annual paid sick leave of 10 days.
• Maternity/Paternity leave.
• The opportunity for sabbatical leave.
• Marriage and Parenthood Package.
• Compensation for sports activities or health insurance covering (up to 250$ per year) — after the trial period.
• Career development plan.
• English and Spanish classes.
• Paying taxes and managing PE (Private Entrepreneur).
• Technical equipment.
• Internal Referral program.
• Opportunity to take part in company volunteering activities.
• Sombra is a “Friendly to Veterans” award-holder.

In this role, you will be a key contributor to the development and evolution of the Satori AI Platform, building next-generation conversational and agentic AI solutions for enterprise procurement and supplier ecosystems.

You will design, develop, and deploy intelligent chatbot solutions using Microsoft technologies, enabling seamless interactions across omnichannel platforms such as Microsoft Teams, web, and enterprise applications. The role involves leveraging NLP, machine learning, and agentic AI frameworks to create context-aware, scalable, and business-driven conversational experiences.

You will also collaborate closely with product, architecture, and integration teams to enhance platform capabilities, optimize existing solutions, and ensure high performance, security, and user experience.

Key Responsibilities
• Design and develop intelligent chatbots for the Satori AI Platform using Microsoft Bot Framework SDK and Azure Bot Services
• Build and manage conversational agents in Copilot Studio / Power Virtual Agents (Agents in Portal)
• Implement Omnichannel solutions across Microsoft Teams, web, and other enterprise channels
• Integrate chatbot solutions with enterprise systems such as SAP S/4HANA, SAP Ariba, ServiceNow, and other APIs
• Design and implement conversational flows using adaptive dialogs and orchestration frameworks
• Develop agentic AI workflows for enterprise use cases such as procurement automation, supplier queries, and compliance tracking
AI Capabilities
Natural Language Understanding (NLU)
Context and memory management
Multi-turn and goal-driven conversations
Retrieval-Augmented Generation (RAG)
• Work with modern Agentic AI frameworks (e.g., Semantic Kernel, LangChain, AutoGen)
• Collaborate with cross-functional teams including architects, UX designers, and backend developers
• Ensure chatbot solutions are scalable, secure, and enterprise-grade
• Monitor, debug, and continuously optimize chatbot performance using telemetry and analytics tools

Qualifications
• Strong experience in chatbot development using Microsoft Bot Framework SDK and Azure Bot Services
• Hands-on experience with Copilot Studio / Power Virtual Agents (Agents in Portal)
• Good understanding of Omnichannel solutions and integrations

Technical Skills
• Strong programming skills in C#, Python, or JavaScript (Node.js)
• Solid understanding of Natural Language Processing (NLP) and tools such as LUIS or equivalent services
• Experience with machine learning concepts and conversational AI design
• Exposure to Agentic AI frameworks and modern AI architectures (preferred)


Additional Skills
• Experience integrating with enterprise platforms (SAP, - ServiceNow, REST APIs, databases)
• Knowledge of cloud platforms (Azure required; AWS exposure is a plus)
• Strong problem-solving and analytical thinking
• Ability to work in a collaborative, fast-paced environment

Education
• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field

The client is one of the largest global independent private equity fund administrators, administering over $300 billion of private capital on behalf of its clients with offices in New York, San Francisco, Boston, Stamford, Dallas, and Luxembourg. They offer private fund sponsors a best-in-class combination of people, process, and technology, enabling fund sponsors to most effectively manage their operational infrastructure, financial reporting, and investor communications.

The client represents the most experienced and longest-tenured team in the private equity fund administration industry, with broad expertise across buyout, funds of funds, real estate, energy, infrastructure, credit, co-investment, hybrid funds, feeder funds, venture capital, retail, and managed accounts.

The project aims to provide well-prepared and formatted analytic data for the customer.

Responsibilities: 

Own the architecture and delivery of LLM-powered chatbot solution for enterprise financial platform. Responsible for orchestration logic, prompt engineering strategy, model governance and technical leadership across AI components.

• Define LLM orchestration architecture (single-agent pattern)
• Design prompt engineering strategy and system prompting
• Define model behavior guardrails and safety controls
• Lead golden dataset validation strategy
• Own AI risk mitigation (hallucination control, determinism limits)
• Collaborate with BE/FE/DevOps on integration
• Technical leadership & code review for AI components

Requirements: 

• 5+ years in AI/ML engineering
• Strong hands-on with LLM APIs Azure AI Foundry
• Prompt engineering in production systems
• Experience with RAG / structured API orchestration
• Understanding of AI governance & enterprise compliance
• Upper-Intermediate or higher level of English

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.

EXITEK team is looking for a Machine Learning Engineer. We're expanding our development team and looking for a passionate, experienced, self-organised candidate to work on our BeautyUp app (https://apps.apple.com/us/app/id1671843069).

Responsibilities:

• Design, develop, and optimize state-of-the-art computer vision pipelines, algorithms and models;
• Conceive and experiment with innovative features and functionalities in the realm of photo editing and enhancement;
• Collaborate with cross-functional teams to integrate machine learning models into production systems and pipelines.

Requirements:

• Proficiency in Python and PyTorch;
• Experience in Computer Vision (GANs, SD, etc.) is a must;
• Deep understanding of the current state of the art in machine learning and computer vision;
• Robust communication skills, complemented by the ability to thrive in a collaborative team environment.

We are looking for Data Scientist to join a Data Team working on innovative AI-driven products. 
 

These roles focus on building AI-powered solutions that solve real business challenges and deliver personalized customer experiences. The team leverages Generative and predictive AI to enhance customer journeys and automate operational processes.

Responsibilities:

• Collaborate closely with Product Owners, Data Analysts, Data Engineers, and ML Engineers to design, develop, deploy, and monitor scalable AI and Generative AI solutions
• Drive the adoption of Generative AI across the organization by building solutions using LLMs, embeddings, and RAG architectures
• Work directly with business stakeholders to translate requirements into production-ready AI systems
• Monitor and optimize model and system performance, applying best practices in Data Science, MLOps, and LLMOps
• Continuously improve prompts, models, and pipelines to maximize business impact
• Stay up to date with advancements in LLMs, RAG frameworks, and GenAI technologies to drive innovation

Requirements:
Must-have:

• 5 years of experience in Data Science or Applied Machine Learning
• Strong proficiency in Python and SQL
• Experience building and deploying machine learning models
• Hands-on experience with Generative AI, including:

• Embeddings
• Vector search
• RAG-style architectures
• NLP or transformer-based models
  • Understanding of advanced model evaluation methods (ranking, relevance, retrieval quality, etc.)

Nice to have:

• Experience with Snowflake
• Knowledge of AWS cloud services
• Experience with LLMOps (prompt and version management, monitoring, evaluation frameworks)

About the role
We’re hiring a Senior AI Engineer to design, develop, and deploy enterprise AI solutions including Agentic AI, intelligent automation, and predictive analytics, aligned with governance/security frameworks and business objectives.

What you’ll do

• Build AI/ML models (classification, prediction, NLP, computer vision) and optimize for performance/cost 
• Build LLM-based solutions (chatbots, copilots, automation agents) 
• Design and implement multi-agent architectures, orchestration logic, decision flows, and human-in-the-loop controls 
• Own data prep, feature engineering pipelines, training/fine-tuning/evaluation, and model lifecycle/versioning 
• Integrate AI into enterprise systems via APIs/microservices, including real-time inference services 
• Implement responsible AI practices (bias control, explainability, auditability) and secure AI pipelines/APIs (auth/authz) 
• Monitor production performance, logging/alerting, drift, and optimize latency/scalability 

Must-have

• 5+ years AI/ML engineering; production deployments; enterprise-scale AI systems/APIs 
• Hands-on LLMs/NLP or Computer Vision 
• Strong Python ML stack (TensorFlow/PyTorch/Scikit-learn) 
• LLM ecosystem experience (Azure OpenAI / OpenAI / Gemini / HuggingFace), embeddings/vector DBs 
• Agentic/orchestration frameworks (LangChain / Semantic Kernel / AutoGen; multi-agent frameworks; tool use) 
• Azure AI Services/Azure ML/Databricks + Docker/Kubernetes; REST/microservices, event-driven, APIM 

Authority / constraints

• Owns AI architecture/model development standards and Agentic AI implementation approaches 
• Cannot deploy without security/governance approval; cannot use external AI services without compliance validation 

Nice-to-have certs

• AI-102 preferred; DP-100 recommended

Хелоу, люба спільното \0/

Ми продовжуємо нарощувати команду американського Survey SaaS, що аналізує поведінку юзерів під час digital опитувань. По суті, це платформа, де бізнес може засетапити своє опитування та отримати швидкий результат, очищений від ботів + отримати advanced behavioural analytics від нашої моделі.

Вам сподобається, якщо ви:
- самодостатні та организовані.
- хочете автономію та відповідальність за свою ділянку.
- вам дійсно цікаво те, чим ви займаєтесь.

На цьому я дозволю перемкнутись на англійську, бо далі конкретніше.

About the role

Build and own ML models that predict survey respondent certainty and detect fraud from behavioral signals — mouse movement, click patterns, timing, browser/OS data. You'll be the sole ML engineer maintaining a production hybrid CNN+tabular certainty model (already in client deliverables) and building the new bot detection model. You'll work directly with a US-based AI scientist who provides strategic direction.
 

What you'll work on

• Improve the hybrid 1D CNN (mouse trajectories) + MLP (tabular features) certainty model — error analysis, hyperparameter tuning, augmentation, loss experiments 
• Build the bot detection classifier — binary human/bot model reusing behavioral pipeline infrastructure 
• Run segment-level evaluations — per-survey quality checks, precision/recall optimization, calibration analysis 
• Track experiments in MLflow — every run logged with parameters, metrics, dataset versions 
• Process raw time-series — mouse trajectories as [T, 7] tensors (x, y, speed, acceleration, dt, dx, dy) 
• Document to audit standard — code, dataframes, notebooks, reproducible results with artifact links
   

What we're looking for

• Strong PyTorch — built and trained CNNs from scratch, not just fine-tuned 
• Tabular ML experience — LightGBM, XGBoost, or gradient boosted trees 
• Rigorous evaluation mindset — calibration, segment analysis, label quality, beyond single-metric optimization 
• Time-series/sequence data experience — temporal patterns in any domain (NLP, audio, sensors, clickstream) 
• Imbalanced classification expertise — precision/recall tradeoffs, threshold tuning, AUC-ROC 
• MLflow or similar (W&B, Neptune) 
• Self-directed — you're the only ML person on execution, strategic guidance from US AI scientist 
• Auditable work — advisor reviews regularly, notebooks/scripts must be clean
• Clean verbal English
   

Nice to have

Behavioral/clickstream analysis • time-series augmentation • fraud/anomaly detection • model serving • survey methodology/psychometrics
 

Stack

Python • PyTorch • LightGBM • MLflow • HDF5 • PostgreSQL
 

What this is NOT LLM/GenAI role, but classical ML + deep learning on structured behavioral data 

The Team

• Product Manager
• 2x Fullstack Devs
• Anti-Fraud Automation QA
• DevOps
   

We offer

• 15 paid day-offs per year.
• 10 US/UA holidays.
• Flexible schedule and fully remote work from anywhere with reliable overlap to CET.
• Senior team members and strong AI Scientist Advisor.
• Minimal bureaucracy, high authonomy and trust, fast decisions.

Responsibilities

• Refactor a single-agent prototype into a multi-agent architecture (orchestrator + specialized agents).
• Design orchestration logic, agent communication, and state/session management.
• Implement guardrails (prompt-injection protection, output validation).
• Integrate external APIs with resilient error handling.
• Optimize performance and latency (<2–4 seconds).
• Implement monitoring, telemetry, and observability for production systems.
• Collaborate with cross-functional teams to ensure robust API design and integration.

Job requirements

• Strong experience with LLM systems and multi-agent architectures.
• Proficient in Python, including FastAPI, async programming, and Pydantic.
• Experience with function calling and JSON schema outputs.
• Strong API design skills and integration experience.
• Experience in Azure environment: AI Foundry, Key Vault, Managed Identity, monitoring.
• Solid experience in system architecture and production deployment.
• English: conversational; German: A2 comprehension.

Nice to Have:

• MCP certification or relevant Azure certifications.

This is not a coding role. This is a systems-of-systems role. 

If you build things with AI, this posting isn't for you. 

If you build the systems that govern how AI builds things — keep reading.

What This Role Is

LifeChef is a HIPAA-compliant healthcare platform delivering Medically Tailored Meals. Our engineering team builds on a methodology called Enso — a spec-driven, AI-augmented development framework where every feature starts with a specification, gets built with AI assistance, and is validated against defined criteria.

Enso v1 is linear: spec → generate → validate → ship. It works. But it's hitting scaling limits — the document graph grows with every feature, validation complexity compounds, and the process requires increasing human oversight to maintain quality. This is a familiar problem in any AI-assisted workflow that outgrows its initial architecture.

We need someone to take Enso from v1 to v2: a true agentic system where specialized AI agents handle research, specification validation, code generation oversight, quality assurance, and deviation detection — with structured autonomy, clear escalation paths, and human-in-the-loop at defined decision points, not everywhere.

You would partner directly with the CTO (who designed the current methodology and has strong opinions about where it needs to go) to architect, build, and maintain this system. The first 30 days are heavy collaboration — translating vision into working architecture. After that, you own the evolution of the process while the CTO focuses on product and team.

What You'd Actually Build and Maintain

• Agent topology for the development lifecycle: research agents, spec validation agents, code review agents, compliance checking agents, red-team/adversarial agents — each with defined boundaries, confidence thresholds, and escalation triggers
• Spec-to-code reconciliation system: agents that validate whether implemented code actually satisfies the specification, flagging deviations and ambiguities rather than silently passing
• Multi-model orchestration strategy: routing different tasks to different models based on cost, capability, and risk (expensive models for reasoning, cheap models for classification, open-source where it fits)
• Structured output validation: ensuring agent outputs are parseable, auditable, and actionable — not just "LLM said it's fine"
• Governance and escalation framework: what agents decide autonomously vs. what requires human sign-off, circuit breakers for low-confidence situations, audit logging for compliance
• Evolution engine: the system should improve itself — tracking where agents fail, where human overrides happen, and feeding that back into prompt refinement and architecture changes
• Integration with existing CI/CD: the agentic layer hooks into GitHub Actions, automated testing (Vitest), PHI scanning, secret detection, and compliance validation already in the pipeline

This is infrastructure that the development team operates within. You're not shipping product features — you're shipping the machine that makes product development faster, safer, and more consistent.

What We Need From You

Hard requirements:

• You have designed or built at least one agentic system where AI agents operate with structured autonomy — multi-step workflows with validation, error handling, and escalation. Not just API wrappers. Not just chatbots.
• You understand agent topology as a design problem: which agents need to exist, what their boundaries are, how they communicate, how you prevent cascading failures.
• You have hands-on experience with LLM orchestration frameworks (LangChain, CrewAI, AutoGen, custom implementations — the specific tool matters less than the depth of understanding).
• You think in terms of eval frameworks and validation: how do you know if an agent's output is good? How do you measure drift? How do you build confidence scoring that's actually useful?
• You understand multi-model strategies: when to use which model, cost/capability tradeoffs, how to tier your model usage so you're not burning expensive tokens on classification tasks.
• You can work in both Python and TypeScript, or at minimum are comfortable in one and willing to operate in the other. Our product stack is TypeScript-first but agentic tooling often lives in Python.
• Strong written communication. This role involves heavy async documentation of architectural decisions, process design, and system behavior.

What sets you apart:

• Experience with Model Context Protocol (MCP) — building custom MCP servers, tool-use architectures
• Claude Code, Cursor, or similar AI-native development tools as part of your own workflow (you should be using what you're building systems around)
• Experience in healthcare, fintech, or regulated environments where AI outputs must be auditable and compliant
• Understanding of prompt engineering at the systems level — not "write a good prompt" but "design a prompt architecture that's maintainable, versionable, and testable"
• You've thought seriously about adversarial validation — red-team agents, cross-model verification, structured disagreement as a quality signal
• You have opinions about where agentic AI is overhyped and where it genuinely works, and you can defend those opinions with specifics

What we explicitly don't require:

• A portfolio of 50 agentic projects. The field is young. One deep project with real orchestration depth beats ten shallow ones.
• Product development skills. You won't be building UI or shipping user-facing features.
• Healthcare domain expertise. We'll teach you the domain. You teach us the orchestration.

How We Evaluate

1. Screening questions (in your application) — We're looking for specificity and honesty, not keyword density.
2. Architecture conversation with CTO (90 min) — We'll describe our current system, its limitations, and where we want to go. You'll tell us how you'd approach it. This is a two-way design conversation, not a quiz.
3. Small design exercise (async, take-home) — Given a simplified version of our spec validation problem, design an agent topology. We're evaluating how you think about boundaries, failure modes, and escalation — not production-ready code.
4. Trial sprint (60–90 days) — Heavy pairing with CTO in the first 30 days, increasing autonomy after. Defined milestones at 30, 60, and 90 days.

The Team and Context

Small, senior engineering team, primarily Ukraine-based. The CTO (Jay) designed the Enso methodology, has 20+ years of engineering experience across healthcare, threat intelligence, and enterprise platforms, and has built the governance architecture (constitutions, spec-gates, validation layers) that you'd be evolving. He's technical, direct, and has strong opinions — but he's hiring you because he needs a partner in this, not an executor.

The product stack is Next.js 16, React 19, TypeScript, PostgreSQL, AWS serverless (Lambda, CDK, Amplify Gen 2), with Vitest testing, Pino structured logging, and HIPAA compliance enforced in CI. You don't need to know this stack deeply, but you need to understand it well enough to build agentic tooling that integrates with it.

We're building toward autonomous agentic systems for clinical operations — this development process work is the foundation that makes that possible. If the idea of designing the system that governs how an engineering team builds software excites you, this is the role.

How to Apply

Reply with:

• The most complex agentic or LLM orchestration system you've built — what it did, how it was structured, what you learned
• Your honest take on where agentic AI is right now and what's actually hard
• What interests you about building process infrastructure vs. product features

Generic responses get skipped. We know the field is young — we'd rather hear about one deep experience than a list of tools you've touched.

Our Customer:

Our client is a technology-focused company building high-performance, real-time ML inference systems. The team develops ultra-low-latency engines that process billions of requests per day, integrating ML models with business-critical decision-making pipelines. They are looking for an experienced backend engineer to own and scale production-grade ML services with strong focus on latency, reliability, and observability.

Your tasks:

• Lead the design and development of low-latency ML inference services handling massive request volumes.
• Build and scale real-time decision-making engines, integrating ML models with business logic under strict SLAs.
• Collaborate closely with data scientists to deploy ML models seamlessly and reliably in production.
• Design systems for model versioning, shadowing, and A/B testing at runtime.
• Ensure high availability, scalability, and observability of production systems.
• Continuously optimize latency, throughput, and cost-efficiency using modern tools and techniques.
• Work independently while collaborating with cross-functional teams including Algo, Infrastructure, Product, Engineering, and Business stakeholders.

Required Experience and Skills:

• B.Sc. or M.Sc. in Computer Science, Software Engineering, or related technical field.
• 5+ years of experience building high-performance backend or ML inference systems.
• Expert in Python and experience with low-latency APIs and real-time serving frameworks (e.g., FastAPI, Triton Inference Server, TorchServe, BentoML).
• Experience with scalable service architectures, message queues (Kafka, Pub/Sub), and asynchronous processing.
• Strong understanding of model deployment, online/offline feature parity, and real-time monitoring.
• Experience with cloud environments (AWS, GCP, OCI) and container orchestration (Kubernetes).
• Familiarity with in-memory and NoSQL databases (Aerospike, Redis, Bigtable) for ultra-fast data access.
• Experience with observability stacks (Prometheus, Grafana, OpenTelemetry) and alerting/diagnostics best practices.
• Strong ownership mindset and ability to deliver solutions end-to-end.
• Passion for performance, clean architecture, and impactful systems.

Would be a plus:

• Prior experience leading high-throughput, low-latency ML systems in production.
• Knowledge of real-time feature pipelines and streaming data platforms.
• Familiarity with advanced monitoring and profiling techniques for ML services.

This is a fantastic opportunity to join a fast-growing company at the heart of the digital revolution. Our software product is revolutionising manufacturing through automation. We are trusted by global brands and work across sectors (mobility, consumer goods, machinery etc).

We have a talented and ambitious team. We value integrity and are passionate about our mission to revolutionise advanced manufacturing.

This is your opportunity to:

• Be part of a fast-growing software product company in advanced manufacturing.
• Learn and fulfil your potential in an open, knowledge-driven culture.
• Share in our ambitious vision and make a meaningful difference towards autonomous manufacturing.

Responsibilities: 

• Develop, test, and deploy generative AI based solutions.
• Design and implement end-to-end AI solutions that integrate with existing systems. 
• Collaborate with software engineers and product teams to understand business needs and translate them into technical solutions. 
• Research emerging AI trends and techniques to continually improve the AI solution offerings. 
• Prepare and maintain documentation on design, code, and processes to ensure organizational knowledge retention. 
• Monitor and evaluate the performance of AI systems in production, identifying areas for improvement or further development.

You will be joining a well-versed team if you have:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field. 
• Proven experience in developing generative AI or machine learning models.
• Commercial experience in using large language models.
• Programming skills in languages such as Python or JavaScript.
• Familiarity with databases (SQL, NoSQL). 
• Solid understanding of deep learning, NLP, computer vision, or other specialized areas within AI is a plus. 
• Experience with cloud platforms (AWS, Google Cloud, Azure) and containerization (Docker, Kubernetes) is beneficial. 
• Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
• Good communication skills and the ability to translate complex technical concepts to non-technical stakeholders. 
• Commitment to continuous learning and development in the field of AI and emerging technologies.

As an ML Engineer, you'll work at the intersection of machine learning systems, knowledge representation, and reasoning infrastructure — building the foundational intelligence layer that powers our platform. This isn't about wrapping APIs or fine-tuning models in isolation. You'll be solving hard problems at the frontier of applied ML.

What you'll work on:

Designing ML pipelines that transform unstructured, proprietary information into semantically rich, queryable representations

Building and integrating models that enable probabilistic reasoning, multi-hop inference, and context-aware decision support

Developing agentic workflows where AI assists human judgment through explainable outputs, uncertainty quantification, and iterative feedback loops

Defining evaluation metrics and testing frameworks for high-stakes, information-led environments

Contributing to architectural decisions around ML infrastructure and model serving

Collaborating closely with backend engineers, product, and domain experts

What We're Looking For

Strong foundations in machine learning — NLP, information extraction, representation learning

Systems thinking mindset, not just model performance in isolation

Comfortable working in ambiguity and defining problems from first principles

Intellectually honest — willing to challenge assumptions, including our own

Pragmatic: ships working systems, iterates on real feedback, avoids over-engineering

Cares about reproducibility, clarity, and long-term maintainability

What We Offer

Work on genuinely hard, foundational problems at the intersection of ML and knowledge systems

Founder-led, deeply technical environment — low-ego, high-ownership

Fast feedback loops, strong opinions, high bar for clarity

Flexible/hybrid setup centred around London

Early-stage equity

Our Values

First-principles thinking. Human judgment matters. Intellectual honesty. Trust by default. Compounding advantage. Build infrastructure, not wrappers.

Responsibilities:

• Own and support core ML infrastructure (MLflow, CVAT, GPU/CPU cloud instances, internal data tools).
• Assist Data Engineers with deploying and maintaining data pipelines.
• Act as the primary contact for ML and Data teams on infra-related requests.
• Improve reliability and efficiency by adding automation, monitoring, and tooling enhancements.
   

Must have:

• Knowledge of Python, Terraform, Docker, Linux.
• Hands-on experience with cloud environments (Azure prefered).
• Understanding of the end-to-end ML lifecycle (data → training → evaluation → deployment → monitoring).

Nice to have:

• Familiarity with Kubernetes.
• Experience with FFmpeg or GStreamer for video processing and streaming.
• Experience with model serving frameworks and inference formats (ONNX, TensorRT, OpenVINO, NVIDIA Triton).
• Experience deploying computer vision models, especially to edge/embedded devices (NVIDIA Jetson, Intel platforms, Raspberry Pi).

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