The role

Token Factory is a part of Nebius Cloud, one of the world's largest GPU clouds, running tens of thousands of GPUs. We are building a high-performance inference and fine-tuning platform designed to push foundation models to their hardware limits. Our mission is to maximize throughput, minimise latency, and optimise cost-per-token across tens of thousands of GPUs.

Some directions we are currently working on, and which you can be a part of:

• Inference Optimization: Identifying LLM inference bottlenecks to drive production speedups. Squeezing the maximum performance for a wide range of LLM architectures at scale (e.g., GPT-OSS, Kimi K2.5, DeepSeek V3.1/V3.2, GLM-5).
• Inference engines support: Implement novel speculative decoding architectures, optimise components of various LLM designs (dense/MoE, autoregressive/parallel), and contribute to open-source inference engines.
• Low Precision Training & Inference: Design and productionise low-precision (FP8, NVFP4/MXFP4) training and inference pipelines with measurable gains in throughput and cost-efficiency.

We expect you to have:

• A profound understanding of theoretical foundations of machine learning and transformer architecture.
• Experience profiling GPU workloads using Nsight, PyTorch profiler, or similar tools
• Understanding of GPU memory hierarchy and compute/memory tradeoffs
• Familiarity with important ideas in LLM space, such as MHA, RoPE, KV-cache, Flash Attention, and quantisation
•  Understanding of performance aspects of large neural network training (sharding strategies, custom kernels, hardware features etc.)
•  Strong software engineering skills (we mostly use Python)
• Deep experience with modern deep learning frameworks
• Proficiency in contemporary software engineering approaches, including CI/CD, version control and unit testing
• Strong communication and leadership abilities

Nice to have:

• Experience working with open-source inference engines (vLLM, SGLang, TensorRT-LLM), including contributions
• Experience with kernel languages or DSLs such as Triton, Cute, CUTLASS, CUDA
• A track record of building and delivering products (not necessarily ML-related) in a dynamic startup-like environment.
• Strong engineering skills, including experience in developing large distributed systems or high-load web services.
• Open-source projects that showcase your engineering prowess
•  Excellent command of the English language, alongside superior writing, articulation, and communication skills.

As a Machine Learning Engineer at MyHeritage, you will play a crucial role in developing and implementing machine learning models that drive our products and user experiences. You will have a significant impact on our large and growing user base by leveraging the latest AI technologies to solve challenging problems at scale. This is a unique opportunity to join a forward-thinking team that is passionate about innovation, data, and machine learning.

What you’ll do

• Design, develop, and deploy machine learning models for various business needs.
• Collaborate with cross-functional teams, product managers, and software engineers, to integrate ML solutions into production systems.
• Experiment with and iterate on model architectures to improve accuracy, efficiency, and scalability.
• Work with large datasets to preprocess, clean, and extract meaningful features for model training.
• Utilize MLOps best practices to automate and streamline the development lifecycle of ML models.
• Monitor model performance and fine-tune for optimization.
• Stay updated with the latest research and advancements in machine learning and AI.

What you bring

Technical skills:

• Strong foundation in machine learning techniques such as supervised and unsupervised learning, deep learning, and vision/NLP experience.
• Proficiency in Python, along with popular ML libraries and frameworks such as PyTorch, Scikit-Learn, pandas, NumPy, SciPy, TensorFlow.
• Experience with data preprocessing and feature engineering for structured and unstructured data.
• Knowledge of model deployment and serving, using tools like Docker, Kubernetes, or cloud-based services (AWS, GCP, Azure).
• Familiarity with MLOps tools for automation and monitoring (e.g., MLFlow, ClearML).
• Experience with SQL for data extraction and analysis (optional).

Experience:

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field.
• 7+ years of experience in software development, including at least 3 years of experience in the machine learning domains.
• Experience working in a B2C company or a similar high-paced environment is an advantage.
• Proven track record of implementing impactful machine learning solutions in production.

Core attributes

• Strong analytical mindset, with the ability to tackle complex problems with creativity and efficiency.
• Excellent communication skills for conveying technical ideas to both technical and non-technical stakeholders.
• High level of attention to detail and quality in your work.
• Ability to work effectively in a team environment, while also being a self-starter when working independently.
• Curious and eager to keep learning about the latest trends in AI and machine learning.
• Comfortable in a fast-paced, dynamic work environment with changing priorities.

 About MyHeritage

MyHeritage is the leading global discovery platform for exploring family history. With billions of historical records and family tree profiles, and with sophisticated matching technologies that work across all its assets, MyHeritage gives users the joy of discovering their past and empowering their future. MyHeritage is the most popular DNA test and family history service in Europe. Since 2020, MyHeritage is also home to the world’s most advanced AI technologies for repairing, enhancing, colorizing, and animating historical photos. Learn more at www.myheritage.com. See what it’s like to work at MyHeritage on www.glassdoor.com.

Benefits at MyHeritage

Our employees enjoy a range of benefits to enhance work-life balance and well-being:

• Hybrid work model
• Modern, beautiful office in Or Yehuda with shuttle service from Tel Aviv and employee parking (for our Or Yehuda employees)
• On-site gym and pilates classes
• Dog-friendly office
• Well-stocked kitchens and a monthly meal allowance 
• Fully funded supplemental health insurance

#LI-Hybrid

About QuantHealth

QuantHealth is a growing AI startup in the clinical trial space, leveraging AI, biomedical data, knowledge graphs, and real-world patient data to simulate and optimize clinical trials for pharmaceutical companies. Our platform combines large-scale biomedical knowledge, clinical trial data, and patient-level data to help customers simulate clinical trials, reduce development risk and cost, shorten timelines, and improve the probability of clinical trial success.

About the Role

As a Data Scientist in the CDS R&D team, you will play a key role in researching, developing, and productionizing advanced data science solutions that power QuantHealth’s clinical simulation and prediction capabilities.

This role is designed for a strong applied data scientist who enjoys solving complex, ambiguous problems end-to-end: from data exploration and feature engineering, through model development and validation, to building scalable pipelines and AI-driven workflows. You will work closely with clinical experts, product, delivery, engineering, and other data science teams to translate challenging clinical and biomedical questions into robust data-driven solutions.

The ideal candidate brings several years of hands-on experience in applied data science, machine learning, and data pipeline development. Additional qualities we look at in candidates are scientific thinking, project ownership, and the ability to independently learn and operate in complex domains. Experience with biomedical or clinical data is a strong advantage.

This position involves working with a modern Python-based data science stack, building predictive models, developing data and ML pipelines, and contributing to the continuous improvement of QuantHealth’s clinical trial simulation product. The role requires curiosity, strong self-learning abilities, research orientation, and rigorous analytical thinking.

Responsibilities

• Design, develop, and validate data science models, algorithms, and tools that support QuantHealth’s internal and customer-facing clinical simulation products.
• Build end-to-end data science solutions, including data ingestion, feature engineering, model development, evaluation, and deployment-ready pipelines.
• Collaborate with clinical, product, delivery, engineering, and data science teams to prototype and implement data-driven solutions.
• Explore, process, and integrate diverse data sources, including clinical trial data, EHR/RWD, biomedical literature, biomedical ontologies, and knowledge graphs.
• Develop scalable ETL and data pipelines to support automated clinical simulation workflows.
• Build LLM-based and agentic systems to automate internal research, data processing, and biomedical reasoning workflows.
• Develop and improve data projects using tools such as PySpark, PyTorch, PyGeometric, MLflow, SQL, and the broader Python data stack.
• Stay current with emerging methodologies in machine learning, LLMs, knowledge graphs, biomedical AI, and applied data science.
• Contribute to internal knowledge sharing, best practices, and scientific discussions within the R&D organization.

Qualifications

• MSc or PhD in data science, computer science, computational biology, or a related quantitative field.
• 4+ years of hands-on experience in applied data science, machine learning, or algorithm development.
• Strong Python skills and practical experience with common data science and ML libraries such as pandas, NumPy, scikit-learn, PyTorch, SQL, and Spark.
• Proven experience building end-to-end data science solutions, from raw data processing to model development, evaluation, and implementation.
• Experience working with large, messy, real-world datasets and transforming them into reliable analytical or machine learning pipelines.
• Strong understanding of machine learning concepts, model evaluation, statistical analysis, and experimental design.
• Experience with cloud-based computing environments and scalable data processing pipelines.
• Strong analytical and problem-solving skills, with attention to details and scientific rigor.
• Strong research mindset and ability to independently learn new domains, methods, and technologies.
• Excellent communication skills and ability to collaborate effectively across clinical, product, engineering, and data science teams.

Strong Advantages

• Experience working with biomedical, healthcare (RWD/EHR), pharmaceutical, biomedical ontologies, or real-world patient data.
• Experience developing LLM-based workflows or agentic pipelines.
• Experience with knowledge graphs, graph embeddings, GNNs, or graph-based ML.
• Experience with PySpark, PyTorch, PyGeometric, MLflow, or similar ML engineering tools.
• Experience creating interactive data applications, dashboards, or internal tools.
• Familiarity with clinical trial design, drug development, computational/system biology, or translational medicine.

Who we are

DV is the leader in digital performance solutions, helping our advertiser and agency partners verify the quality of their digital campaigns, Optimise to improve performance and Prove that they’re achieving their business outcomes, through unbiased 3rd party data and analytics. DV’s mission is to be the definitive source of transparency and data-driven insights into the quality and effectiveness of digital advertising for the world’s largest brands, agencies, publishers, and digital ad platforms.  Since 2008, DV has helped hundreds of Fortune 500 companies gain the most from their media spend by delivering best-in-class solutions across the digital advertising ecosystem, helping to build a better industry. Learn more at www.doubleverify.com.

What you will do

You will lead a team of experienced Data Scientists while remaining deeply involved in the technical work.

This is a hands-on leadership role (~70% hands-on) combining direct modeling work with ownership of team direction and execution.

You will work on core systems that operate at a massive scale, where:

• Data is abundant, but labels are scarce and expensive

• problems are long-tail and ambiguous

• Systems must meet strict latency and cost constraints (pre-bid)

Your responsibilities include:

• Lead development of content classification systems across social platforms (Meta, TikTok, YouTube), web, and apps

• Design and build models across computer vision, NLP, and multimodal pipelines

• Own the full lifecycle: data selection -> labeling strategy -> training -> evaluation -> deployment

• Develop strategies for efficient data curation and labeling (active learning, auto-labeling, sampling under scale)

• Improve model quality (precision/recall) while balancing cost, latency, and scale

• Drive automation systems (auto-labeling, auto-curation, retraining loops)

• Apply modern AI approaches (LLMs, embeddings, foundation models) to real production problems

• Lead and mentor a team of senior Data Scientists, setting technical direction and pushing execution forward

• Work closely with ML Engineering, Product, and Policy to translate ambiguous requirements into scalable systems

Who you are

• 3+ years of experience leading Data Science / ML teams

• 6+ years of hands-on experience in Machine Learning / Deep Learning

• Strong background in Computer Vision and/or NLP

• Experience building and deploying production ML systems at scale

• Strong understanding of real-world trade-offs (accuracy, cost, latency)

Technical requirements

• Hands-on experience with deep learning frameworks (PyTorch / TensorFlow)

• Experience with ML/DS tools (scikit-learn, OpenCV, HuggingFace, etc.)

• Experience working with large datasets and model evaluation pipelines

Advantages

• Experience with multimodal systems (vision + text + audio)

• Experience with LLMs / embeddings / foundation models

• Experience with AutoML, active learning, or data-centric AI

#Hybrid#
 

The opportunity
Join a high-impact ML team building state-of-the-art systems that optimize experiences for billions of users. You'll tackle challenging problems across diverse datasets - with the freedom to explore novel approaches and infrastructure to deploy them at scale.

What Success Looks Like:

Short-term: Ship your first production model with measurable impact within 3 months.

Long-term: Own major ML initiatives end-to-end and introduce new methodologies that expand the team's capabilities.

Your Impact:

Elevate our technical bar with fresh ML perspectives. Shape our roadmap by identifying opportunities others miss. Your research skills keep us cutting-edge while your execution speed drives results.

What You'll Gain:

Work on real-time ML systems at scale with rich, diverse data

Autonomy to define problems and drive projects from idea to impact

Freedom to explore emerging techniques and expand your scope

Fast feedback loops through strong experimentation infrastructure

What you'll be doing

• Join a high-impact ML team building state-of-the-art systems that optimize experiences for billions of users. You'll tackle challenging problems across diverse datasets - with the freedom to explore novel approaches and infrastructure to deploy them at scale.
• What Success Looks Like:
• Short-term: Ship your first production model with measurable impact within 3 months.
• Long-term: Own major ML initiatives end-to-end and introduce new methodologies that expand the team's capabilities.
• Your Impact:
• Elevate our technical bar with fresh ML perspectives. Shape our roadmap by identifying opportunities others miss. Your research skills keep us cutting-edge while your execution speed drives results.
• What You'll Gain:
• Work on real-time ML systems at scale with rich, diverse data
• Autonomy to define problems and drive projects from idea to impact
• Freedom to explore emerging techniques and expand your scope
• Fast feedback loops through strong experimentation infrastructure

What we're looking for

• Strong Deep Learning Expertise – Comprehensive knowledge of DL theory paired with the practical intuition gained from training many networks; comfortable working with SOTA pretrained models while understanding the underlying mechanics of convergence and scale.
• Proven ability to deliver production ML models with measurable business impact - from problem definition through A/B testing -> deployment and outcome tracking
• AI-Native Builder Mentality – Comfortable breaking conventional development cycles to build fast and ship often. You have a deep mastery of the modern GenAI stack for development, using it to automate the mundane and focus your cognitive load on high-level architecture and model performance.
• Strong programming skills in Python and Deep Learning stack (PyTorch, scikit-learn, XGBoost) with ability to write clean, efficient code for production systems
• Self-directed research capability - demonstrated ability to independently explore unfamiliar ML domains, synthesize technical literature, and implement novel approaches
• Structured problem-solving methodology - ability to decompose complex business problems, design solution approaches, and communicate technical findings to stakeholders

You might also have

• Positivity and enthusiasm

Additional information

• Relocation support is not available for this position
• Work visa/immigration sponsorship is not available for this position

Benefits
At Unity, we want our team members to thrive. We offer a wide range of benefits designed to support well-being and work-life balance.

Please note: Benefits eligibility, specific offerings, and coverage vary based on the country and employment status.

While specific benefits vary, here are some of the ways we strive to take care of our eligible team members globally: Comprehensive health, life, and disability insurance | Commute subsidy | Employee stock ownership | Competitive retirement/pension plans | Generous vacation and personal days | Support for new parents through leave and family-care programs | Office food snacks | Mental Health and Wellbeing programs and support | Employee Resource Groups | Global Employee Assistance Program | Training and development programs | Volunteering and donation matching program

Life at Unity
Unity [NYSE: U] is the world’s leading game engine, powering play for more than 3 billion consumers each month. The top mobile games in the world, the most played PC indie titles, the most innovative console games, and virtually all of the top XR and Web Games are developed, deployed, and grown in Unity. Unity also enables teams across industries like automotive, manufacturing, and healthcare to design, simulate, and collaborate in 3D — closing the gap between ideas and reality. For more information, please visit www.unity.com.

Unity is a proud equal opportunity employer. We are committed to fostering an inclusive, innovative environment and celebrate our employees across age, race, color, ancestry, national origin, religion, disability, sex, gender identity or expression, sexual orientation, or any other protected status in accordance with applicable law. Our differences are strengths that enable us to support the growing and evolving needs of our customers, partners, and collaborators. If you have a disability that means there are preparations or accommodations we can make to help ensure you have a comfortable and positive interview experience, please fill out this form to let us know.

This position requires the incumbent to have a sufficient knowledge of English to have professional verbal and written exchanges in this language since the performance of the duties related to this position requires frequent and regular communication with colleagues and partners located worldwide and whose common language is English.

Headhunters and recruitment agencies may not submit resumes/CVs through this website or directly to managers. Unity does not accept unsolicited headhunter and agency resumes. Unity will not pay fees to any third-party agency or company that does not have a signed agreement with Unity.

Your privacy is important to us. Please take a moment to review our Prospect Privacy Policy and Applicant Privacy Policy. Should you have any concerns about your privacy, please contact us at DPO@unity.com.

#SEN

We are looking for a highly skilled Senior Machine Learning Engineer to lead our transition from on-demand, third-party LLM APIs to a fully self-hosted, scalable model ecosystem.

Our core product is an advanced, agentic support chatbot capable of complex reasoning, API tool calling, database lookups, and orchestrating specialized Small Language Models (SLMs) for targeted NLP tasks. As we scale, our current deployment infrastructure (AWS SageMaker) is becoming unsustainable. You will be responsible for architecting, deploying, and optimizing an infrastructure capable of supporting 50 to 100 distinct models ranging from 100M to 70B parameters.

What You’ll Do:

• Inference Optimization: Deploy and manage large-scale models using high-performance inference engines (like vLLM) to ensure low latency and high throughput for our agentic chatbot.
• Agentic Workflows: Develop and refine the chatbot's agentic capabilities, ensuring reliable tool-use, routing, and interactions between massive LLMs and specialized SLMs.
• Model Fine-Tuning: Design and execute fine-tuning strategies to improve model accuracy on specific domain tasks and tool-calling execution.
• Rigorous Evaluation: Build comprehensive offline and online evaluation frameworks to constantly measure model performance and business impact through structured A/B testing.

What We’re Looking For:

Core Engineering & AI Frameworks

• Strong proficiency in Python and Bash scripting.
• Deep experience with PyTorch and the Hugging Face ecosystem.
• Experience using AI coding assistants natively in the terminal, specifically Claude Code, to accelerate development workflows.

LLMs, Inference & Agents

• Proven experience deploying models using vLLM, TGI, or similar high-performance inference servers.
• Strong fundamental understanding of LLM architectures, attention mechanisms, and generation parameters.
• Hands-on experience building Agentic systems (ReAct, function/tool calling, RAG).
• Expertise in fine-tuning strategies (e.g., SFT, RLHF, DPO) and parameter-efficient techniques (PEFT/LoRA).

Statistics & Model Evaluation

• Offline Metrics: Deep understanding of classification/summarization metrics (Precision, Recall, F1, AUC) and retrieval metrics (MRR, NDCG, Precision/Recall @ k).
• Online Metrics & A/B Testing: Strong statistical foundation to design and analyze A/B tests safely, including the use of t-tests, Mann-Whitney U tests, and bootstrapping techniques.

Bonus Points

• Containerization & Orchestration: Experience with Ray for orchestrating large-scale model deployments across multi-GPU clusters.
• Model Quantization: Experience with memory optimization techniques like AWQ, GPTQ, GGUF, or FlashAttention to fit 70B models efficiently onto hardware.
• API Development: Proficiency in building robust, asynchronous microservices using FastAPI to serve model requests.

• Knowledge of Data Engineering principles: dataset collection, cleaning, processing, and scalable storage.

Experience with core MLOps practices, including dataset versioning (e.g., DVC), experiment tracking (e.g., Weights & Biases, MLflow), and model registries.

The role 

We are hiring Senior and Staff ML Engineers that sit within Safety Systems / autonomy organisations, building critical behaviours for Level 2 to level 4 driving. It is highly product-focused: you will train and iterate on end-to-end driving models and get them shipped into the car on a fast timeline measured in months. The opportunity is to own the full loop from data and training through evaluation and on-road validation, with direct impact on high-priority commercial deliveries (including the Nissan MVP). You will join a small, high-ownership teams at the point of rapid growth, where execution and real-world outcomes matter.

Key responsibilities

• Develop the AI driver model architecture and training algorithms to introduce and enhance the safety behaviors for L2/L3/L4
• Own key parts of the training model lifecycle, including evaluation strategy, success metrics, and iteration planning.
• Mine, bucket, and curate real-world and synthetic data to teach specific driving behaviours, and implement data schemes to support training.
• Run and analyse on-road and offline experiments, translate results into clear next steps, and drive improvements through repeated training cycles.

About you  

In order to set you up for success as a Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  

Essential

• Proven experience training deep learning models, with clear end-to-end ownership (data, training, evaluation, iteration).
• Proven Experience taking ML models into production, including working through real-world constraints and quality and safety requirements.

Desirable

• Reinforcement learning experience (especially where it materially improved real-world performance).
• Experience with end-to-end driving models and / or transformer networks 
• Automotive or OEM experience, or prior work that involved deploying ML into safety-critical systems.
• Experience with a Pytorch lightning training infrastructure 

This is a full-time role based in our office in Israel.  At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home.   

The Data Science role at Armis is the engine behind our industry-leading Exposure Management (EM) platform.
You will be responsible for the AI models that power our CAASM (Cyber Asset Attack Surface Management) capabilities and our advanced Vulnerability Prioritization and Remediation engines.

We are building the intelligence layer that helps organizations implement a Continuous Threat Exposure Management (CTEM) framework. You will build models that normalize fragmented data from hundreds of sources, calculate complex risk scores, and prioritize which exposures actually pose a threat to the business.

We are looking for an experienced and creative data scientist, with a software development touch, to join our Data Science and Armis family. A data scientist that understands and loves data, and lots of it.

 What you will do...

• Solve Applied Product Challenges: Deep-dive into the Armis product ecosystem to identify and solve high-impact problems. You will translate complex customer needs into scalable ML features that directly improve the user experience.
• Lead Exposure Intelligence R&D: Develop features that unify asset data (CAASM) with threat intelligence. This includes building models for entity resolution (deduplicating assets across fragmented sources) and automated risk assessment.
• Advanced NLP & Knowledge Extraction: Use NLP and LLMs to parse unstructured security data—such as CVEs, threat intel feeds, and security advisories—to automate the mapping of vulnerabilities to specific business contexts.
• Predictive Prioritization: Design and optimize algorithms that go beyond static CVSS scores. You will incorporate exploitability (EPSS), reachability, and business criticality to help clients focus on the 1% of exposures that matter most.
• End-to-End Ownership: Work closely with Product Managers and Data analysts and Engineers to ensure your models aren't just accurate in a notebook, but are robust, explainable, and deliver clear value within the product UI.
• Graph-Based Attack Surface Mapping: Identify hidden patterns and relationships between assets, users, and vulnerabilities to visualize the potential "blast radius" of a security gap.

 What we expect...

• Academic Background: M.Sc./PhD in Computer Science, Statistics, Engineering, or a related field (or equivalent high-level professional experience).
• Industry Experience: 6+ years in Data Science, with a heavy focus on NLP and solving complex, real-world problems using ML/Deep Learning.
• Technical Mastery: Hands-on experience with PyTorch, Hugging Face, scikit-learn, and SQL. Familiarity with processing large-scale datasets (PySpark, or similar) is highly valued.
• Domain Awareness: Proven ability to apply statistical modeling to cybersecurity, risk management, or complex system analysis. Experience with Vulnerability Management or Graph Theory is a significant plus.
• Product and collaborative Driven Mindset: You are obsessed with understanding the "Why" behind the data. You enjoy learning the nuances of the product and the cybersecurity domain to ensure your DS solutions are highly applicable.

Curiosity & Grit: A passion for diving into "messy" data and finding the signal within the noise of the modern attack surface.

We offer the industry’s only platform that fuses customer identity and anti-fraud solutions – customer identity management, identity verification, and fraud prevention. 

We sell to industries with large, consumer-facing businesses such as: banking, financial services, insurance, fintech, gaming, ecommerce/retail, telco / media, utilities, etc.

About the Role:

As the AI Backend engineer  at Transmit Security, you will join a team of highly skilled machine learning engineers in developing and deploying advanced AI/ML solutions that power our identity and security products. You’ll utilize technical skills to drive innovation, ensure delivery of high-impact projects, and scale our data-driven capabilities across the organization.

This role requires both strategic thinking and hands-on expertise. You’ll be responsible for shaping the data science roadmap, mentoring a growing team, and collaborating with product, engineering, and business stakeholders to translate business challenges into practical machine learning solutions.

What you’ll do:

• Design, develop, and maintain backend services for AI agents and tool integrations using latest technologies
• Build scalable APIs and microservices that interface with LLMs and AI frameworks
• Implement agent orchestration systems, tool calling mechanisms, and workflow engines
• Optimize performance and reliability of AI-powered applications at scale
• Develop data pipelines for training, evaluation, and monitoring of AI systems
• Integrate with various LLM providers (OpenAI, Anthropic, etc.) and manage API interactions

What you’ll need:

• Excellent coding skills in Python/TypeScript, with at least 5 years of hands-on experience building reliable backend services, agents and tooling. Familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) is a strong advantage.
• Experience designing, deploying, and maintaining production systems that integrate ML components, including APIs, microservices, model serving layers, feature pipelines, monitoring, and CI/CD/MLOps workflows.
• Solid experience with AI related contexts Understanding of prompt engineering and LLM optimization techniques, RAG architecture
• Solid understanding of distributed systems concepts, performance optimization, observability, and operating services at scale.
• Strong communication skills, with the ability to bridge technical, product, and business perspectives.
• Prior experience in cybersecurity, fraud prevention, or identity management is a plus, especially with secure system architectures or ML-augmented decisioning systems.

Advantages

• Experience integrating with LLM APIs (OpenAI, Anthropic Claude, etc.)
• Experience with agent frameworks (LangChain, LlamaIndex, AutoGPT)
• Background in ML/AI concepts and model deployment
• Experience with message queues (RabbitMQ, Kafka) and event-driven architectures
• Experience with function calling and tool use patterns in LLMs

#LI-AM1 #LI-Hybrid 

#LI-TL1 #LI-Hybrid