We are launching a strategic pool of AI specialists to accelerate the growth of our AI Practice and turn strong market demand into scalable, repeatable capabilities. This role goes beyond project delivery-it is about shaping how AI is built, shared, and scaled across the company.

As part of this group, you will play a key role in internal capability development, with a strong focus on designing and evolving AI Accelerators-reusable frameworks, templates, and reference architectures covering GenAI, LLM-based solutions, ML pipelines, and cloud-native AI systems. These accelerators will become the backbone of our AI offerings, enabling faster delivery, higher quality, and consistent technical standards across projects.

A second core dimension of the role is knowledge leadership. Strategic hires will actively contribute to internal knowledge-sharing initiatives: technical deep dives, hands-on workshops, demos, and best-practice sessions covering topics such as GenAI orchestration, RAG, model deployment, prompt and system design, observability, and cost-efficient AI architectures. You will help raise the overall AI maturity of the organization while mentoring engineers and influencing technical direction. In addition, members of this group will act as AI project jumpstarters, joining new client engagements for up to 50% allocation over 1-2 months. In this capacity, you will help define solution architectures, validate technical approaches, build initial prototypes, and set strong engineering foundations before teams scale. This gives you exposure to diverse domains and cutting-edge AI use cases while maximizing your impact across multiple initiatives.

Project Description

We are looking for a highly skilled and innovative Machine Learning Engineer to develop and expand our Generative AI and Computer Vision capabilities. This role involves accelerating Technical Sales Support, Architecture and PoC development.

Technologies

• Python
• Git, CI/CD practices for ML and data pipelines
• AWS and/or Azure
• PyTorch, TensorFlow
• MLflow
• LLMs and NLP tasks
• Generative AI concepts
• LangChain/LangGraph, LlamaIndex
• Qdrant, Pinecone, Weaviate, FAISS or similar
• RNNs, Transformers
• Dataset labeling, annotation tools (e.g., Label Studio, CVAT)
• Prometheus, Grafana, DataDog, OpenTelemetry
• YOLO, SDD
• Segmentation models
• Image Classification

What You'll Do

• Lead the design, development, and deployment of scalable ML services;
• Architect and implement AI/ML solutions, ensuring model quality, governance, and security;
• Optimize ML workflows for performance and cost efficiency on cloud platforms (AWS, Azure, or GCP);
• Collaborate with data scientists, analysts, and business stakeholders to understand ML requirements;
• Mentor and guide junior engineers, promoting best practices in ML engineering;
• Develop and maintain documentation, ML models, and technical standards;
• Monitor, troubleshoot, and resolve issues in production for ML pipelines and environments;
• Stay up-to-date with emerging trends and technologies in AI/ML field;

Job Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field;
• 5+ years of experience in ML engineering, with at least 1 years of hands-on experience with LLMs;
• Strong proficiency in Python;
• Experience with cloud data platforms (AWS and/or Azure);
• Familiarity with CI/CD, DevOps, and infrastructure-as-code practices for ML engineering;
• Experience with data governance, security, and compliance in cloud environments;
• Excellent problem-solving, communication, and leadership skills;
• English: Upper Intermediate level or higher;

What Do We Offer

The global benefits package includes:

• Technical and non-technical training for professional and personal growth;
• Internal conferences and meetups to learn from industry experts;
• Support and mentorship from an experienced employee to help you professional grow and development;
• Health insurance;
• English courses;
• Sports activities to promote a healthy lifestyle;
• Flexible work options, including remote and hybrid opportunities;
• Referral program for bringing in new talent;
• Work anniversary program and additional vacation days.

In this role, you will design, build, and improve machine learning models for cybersecurity and malware-related use cases. You will focus on experimenting with model architectures, preparing and evaluating data, and supporting the deployment and integration of ML solutions into production systems. You will work closely with engineers and product teams to ensure models are robust, scalable, and impactful.

If you’re interested in working as a Machine Learning Engineer at OPSWAT, please apply, and we will get in touch!

You Will Have the Opportunity to

• Train, test, and optimize machine learning models for cybersecurity and malware detection use cases.

• Experiment with and evaluate different model architectures, including classical ML and deep learning approaches.

• Perform data exploration, feature engineering, and data preprocessing on large, complex datasets (structured and unstructured).

• Design and execute model evaluation pipelines, including performance analysis, validation strategies, and benchmarking.

• Collaborate with data engineers and threat researchers to improve data quality, feature sets, and model performance.

• Support the deployment, integration, and monitoring of ML models in production environments.

• Contribute to building scalable ML workflows and improving model development lifecycle practices.

• Work with cross-functional teams to translate business and product requirements into ML solutions.

What We Are Looking For

• Degree in Computer Science, Mathematics, Engineering, AI, or a related field.

• Solid understanding of machine learning algorithms, model training, evaluation techniques, and optimization methods.

• Hands-on experience with Python and common ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).

• Experience with data exploration, feature engineering, and working with real-world datasets.

• Familiarity with model evaluation metrics and validation strategies.

• Basic understanding of deploying and integrating ML models into production systems.

• Experience with cloud platforms (AWS preferred) is a plus.

• Familiarity with MLOps practices and tools is an advantage.

• Strong problem-solving skills, attention to detail, and willingness to experiment and learn.

• Ability to communicate findings and collaborate effectively within cross-functional teams.

About the role:

• Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration.
• Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements.
• Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation.
• Partner with applied research to translate experimental improvements into production-ready systems.
• Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals.
• Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale.
• Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Deep experience with model evaluation, benchmarking, and error analysis for ML systems.
• Proficiency with ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face).
• Solid understanding of modern ML techniques, including transformer-based models and large-scale training.
• Experience building data pipelines and tooling for large-scale experimentation and quality analysis.
• Strong passion for improving real-world AI system quality, with a track record of delivering measurable, production-grade improvements.

About the role:

• Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration.
• Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements.
• Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation.
• Partner with applied research to translate experimental improvements into production-ready systems.
• Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals.
• Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale.
• Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Deep experience with model evaluation, benchmarking, and error analysis for ML systems.
• Proficiency with ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face).
• Solid understanding of modern ML techniques, including transformer-based models and large-scale training.
• Experience building data pipelines and tooling for large-scale experimentation and quality analysis.
• Strong passion for improving real-world AI system quality, with a track record of delivering measurable, production-grade improvements.

About the role:

• Lead ASR quality improvement efforts, including error analysis, dataset curation, metric definition (e.g., WER and task-specific metrics), and model iteration.
• Analyze large-scale speech and text data to identify failure modes and drive targeted model and data improvements.
• Develop, train, and deploy machine learning models for speech recognition and downstream tasks such as classification, entity recognition, information extraction, and structured insight generation.
• Partner with applied research to translate experimental improvements into production-ready systems.
• Collaborate with product managers, platform engineers, and UX teams to align model quality metrics with customer and business goals.
• Optimize ML pipelines and evaluation workflows to operate efficiently and reliably at scale.
• Establish best practices for model validation, offline/online evaluation, and continuous quality monitoring in production.

• Deep experience with model evaluation, benchmarking, and error analysis for ML systems.
• Proficiency with ML frameworks and libraries (e.g., PyTorch, TensorFlow, Hugging Face).
• Solid understanding of modern ML techniques, including transformer-based models and large-scale training.
• Experience building data pipelines and tooling for large-scale experimentation and quality analysis.
• Strong passion for improving real-world AI system quality, with a track record of delivering measurable, production-grade improvements.

Senior Python AI Software Engineer (Contract, Romania, Remote + Travel)

This is a full time contract opportunity for those based in Romania This role is suitable for candidates based in Romania but will require occasional travel to London, UK. 

About Nearform

Nearform is an independent team of data & AI experts, engineers, and designers who build intelligent digital solutions and capability at pace. We create AI-enabled solutions that enhance digital experiences, empower developers, and deliver measurable results. In these ways, we partner with ambitious enterprises to deliver enduring impact.

Our deep expertise in solving the world’s most complex digital problems, along with our collaborative, people-first approach, enables enterprises to build breakthrough products and modernise legacy systems by unleashing the power of AI.

Today, our team of 500 experts in 20+ countries is trusted by leading enterprises including Lululemon, Puma, Sun Life, Starbucks, Travelex, Virgin Media 02, and Walmart.  

As part of our Engineering function, you will work closely with clients to translate complex business requirements into innovative digital products.
Whether you are a Senior Engineer, Tech Lead, or DevOps Engineer, you will collaborate with distributed teams, leveraging cutting-edge technologies to build secure, high-performance applications. Our Nearform engineers are known for building high quality, scalable solutions that solve real world problems.

The Role

As a Senior Python Developer at Nearform your main task will be designing & building applications using Python. However, as you’ll likely work on a variety of projects your responsibilities may also include:

• Supporting component design, development and maintenance and taking responsibility for personal technical quality standards within the project team.
• Assisting with defining structured practices, especially in source code management, building and deployment.
• Contributing to Nearform tooling and open-source projects.
• Designing and implementing data storage solutions.
• Optimising performance in applications for maximum speed and scalability.
• Using appropriate tools to maintain version control and build processes.
• Getting feedback from users and clients, and building solutions for them. 
• Assisting with the analysis of client requirements.
• Working with and supporting the Delivery Architect (team lead) in project execution and timely delivery.
• Collaborating with client teams.

What we are looking for in this person

• Significant experience delivering at a Senior Developer level in an enterprise environment.
• Practical experience of delivering in an agile environment.
• Practical experience of knowledge of developing real-world solutions and platforms.
• Deep understanding of Python.
• Strong experience using modern AI tools at multiple stages of the development cycle
• Deep understanding of SQL Databases (PostgreSQL is preferred).
• Experience with LangChain, AutoGen, or similar frameworks for multi-agent systems.
• Deep understanding of versioning control tools - Git in particular.
• Good understanding of security and performance considerations.
• Understanding of layout aesthetics, responsive design and mobile web development.
• Understanding of architectural and design patterns.
• General familiarity with a wide variety of open-source technologies and tools.
• Excellent analytical and multitasking skills.
• Excellent communication and collaboration skills.
• Professional proficiency in English.
• Experience with spec driven development tools - highly desirable 
• Available to travel onsite to London, UK occasionally 

Nice to have

• • Experience in Python and AI/ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
  • Knowledge of Data Science techniques, including data preprocessing, feature engineering, and model evaluation.

Our Interview Process

1. 1. Talent Call (30 mins via Zoom)
      An introductory conversation with a member of our Talent Team to learn more about your background, experience, and career goals. We'll also provide a high-level overview of Nearform and answer any questions you may have. This step helps us assess initial alignment.

      ---

   2. Technical Assessment (1 hour via Zoom)
      A live coding challenge with one of our trained interviewers. You'll be asked to build a working solution to a problem via screen sharing, demonstrating your Python expertise, problem-solving ability, and coding best practices in a real-time environment.

      ---

   3. Hiring Manager Interview (45 mins via Zoom)
      A conversation with a Hiring Manager focused on your approach to teamwork, client collaboration, and problem-solving in an enterprise development setting. We'll also share more details about Nearform and the potential projects you may join.

There may also be an additional interview with the client depending on the project

Ready to Join Us?

If you're passionate about building high-quality applications, solving complex challenges, and driving innovation in enterprise development, we’d love to hear from you.

Apply now via our careers page at Nearform and be part of a team that values technical excellence, collaboration, and continuous learning.

Nearform is committed to shaping a better world in all that we do. Our global team is built based on respect, inclusivity, diversity and excellence. 

If you require any adjustments to the interview process as part of your application, please let someone from our Talent Team know so they can help.  

We are establishing a AI Engineering team who will work closely with our most strategic customers to deeply understand their use cases and design, build, and implement products for them. As one of our founding AIs, you will deploy these solutions on the latest infrastructure for the world's most influential businesses, driving real business impact through the newest models and our own innovations in technical delivery.

AI Engineers’ responsibilities look similar to those of a hands-on AI startup CTO: you’ll work in small teams to own delivery of high stakes projects with clients. A day’s work may include building LLM workflows on a large scale, interacting with customers to understand their needs and set their AI strategy, but the most impact will be driven by implementing solutions into the real world of our partner's organizations.

We are looking for customer-focused software engineers to build effective custom software that leverages leading AI models and technology to solve real customer problems. You will partner directly with customers to conceptualize, design, and implement cutting-edge AI solutions. This role focuses on leveraging generative AI models, agentic systems, and iterative prototyping to build impactful proofs of concept (PoCs) that address unique business challenges. The AI engineer acts as a bridge between advanced AI technologies and practical enterprise applications, ensuring that solutions are both innovative and actionable.

You will collaborate closely with Sales, Solutions Engineering, Solutions Architects, and Customer Success Managers who work on the same account. You will also be self driven to learn about the AI market and bring forward that knowledge to improve the way in which we deliver to customers.

Responsibilities

Customer Engagement:

Collaborate deeply with clients to understand their strategic objectives, technical requirements, and operational constraints.

Serve as a trusted advisor for AI strategy, guiding clients through the process of ideating and implementing generative and agentic AI solutions.

Facilitate workshops and brainstorming sessions to identify high-value use cases for AI technologies.

AI Solution Development:

Design, prototype, and deploy AI-powered PoCs using generative models (e.g., GPT, DALL-E) and agentic systems tailored to specific client needs.

Build end-to-end pipelines for deploying AI solutions on client infrastructure, ensuring scalability, security, and performance.

Experiment with cutting-edge methods in natural language processing (NLP), computer vision, reinforcement learning, and multi-agent systems to deliver innovative solutions.

Iterative Prototyping:

Use an experiment-driven approach to rapidly test hypotheses and refine prototypes in collaboration with client teams.

Develop reusable frameworks and tools to accelerate the deployment of generative AI solutions across diverse industries.

Cross-Functional Collaboration:

Work closely with Sales, Applied Research, Product Development, and Customer Success teams to ensure seamless project execution.

Provide actionable feedback from customer engagements to inform the development of new AI capabilities.

Knowledge Sharing:

Document best practices and lessons learned from client projects to contribute to internal knowledge bases.

Mentor junior engineers on the application of generative and agentic AI technologies in real-world scenarios.

Requirements 

Technical Expertise:

Strong proficiency in programming languages such as Python or JavaScript, with experience in frameworks like TensorFlow or PyTorch.

Hands-on experience with generative AI models (e.g., GPT-4/5), agentic systems, or reinforcement learning frameworks.

Familiarity with deploying machine learning models in production environments using cloud platforms (e.g., AWS, Azure) or on-premise systems.

Problem-Solving Ability:

Proven ability to translate abstract business challenges into concrete technical solutions using generative AI.

Strong analytical skills to evaluate model performance and optimize for specific use cases.

Client-Facing Skills:

Exceptional communication skills for explaining complex AI concepts to diverse stakeholders.

Ability to build trust-based relationships with clients and navigate ambiguous problem domains collaboratively.

Adaptability:

Flexibility to work across industries such as healthcare, finance, defense, or retail while tailoring solutions to each domain.

Willingness to travel or work on-site with clients as required.

Requirements (Education, Certification, Training, and Experience)

Bachelor’s degree in computer science or related field or equivalent experience

At least 3 years of experience in software engineering or applied machine learning roles.

Direct experience working with customers on the development of AI-driven applications is highly desirable

Passion for exploring the frontiers of generative and agentic AI technologies.

Entrepreneurial mindset with a focus on delivering measurable business impact through innovative solutions.

Commitment to continuous learning and staying at the forefront of AI advancements.

This role is ideal for those who thrive at the intersection of technology innovation and customer engagement. You will have the opportunity to shape the future of enterprise AI adoption while solving some of the most complex challenges faced by leading organizations.

About the job

The world’s most critical--and at risk--business applications have been neglected for far too long. Onapsis eliminates this blind spot by providing cybersecurity solutions dedicated to business-critical applications. Whether running on premises, in the cloud, or in a hybrid environment, Onapsis helps nearly 30% of the Forbes Global 100 understand the threats and risks across their SAP and Oracle landscapes.

We are seeking a Senior Data Engineer to join our mission-driven team. This role is ideal for experienced data engineers with a proven track record in architecting scalable data pipelines, leveraging cloud technologies, and contributing to high-impact cybersecurity solutions. You will be responsible for building high-performance ETL frameworks, optimizing data platforms, and contributing directly to the enhancement of our customers' threat detection, response, and remediation capabilities.

What you will be doing, your legacy: 

You will be working directly with company Principal Engineers evaluating, scoping, proposing, and building features to fulfill business solution requirements to protect our customers. You will play a direct role in laying the technical foundation for a new product offering. Additionally, you will be working with Engineering and DevOps to deliver high-quality products and services while also working closely with security and IT professionals to ensure safe and secure best practices are followed. 

Responsibilities:

• Architect and Design Scalable Data Solutions: Design/develop/maintain Data lakehouse solutions (Iceberg/Delta Lake /Hudi) applying industry best practices and structuring / optimizing the data according to data access patterns.
• Data Pipeline Development: Implement ETL/ELT pipelines using cloud technologies (Spark / pySpark / Glue, Kinesis Streams / Iceberg) to load the data into a Lakehouse for both efficient ML processing and UI reporting.
• Implement data models and data processing frameworks (Spark, Kafka, Snowflake) to ingest, transform, and load large datasets into Data Lakehouse techs (Apache Iceberg, Apache Delta Lake or Apache Hudi), ensuring high availability and reliability of data.
• Advanced Data Integration: Develop solutions that integrate multiple data sources into Snowflake or similar data warehouses to enable real-time analytics and reporting across dashboards.
• AI/ML Integration: Collaborate with cross-functional teams to co-develop AI-driven features identifying patterns and anomalies in client data using AI/ML technologies (python).
• Compliance and Security: Ensure compliance with industry standards and secure best practices (SOX, SOC 1/2), by implementing data governance frameworks, monitoring data pipelines, and optimizing cloud database architectures to protect sensitive information.
• Stakeholder Collaboration: Work closely with stakeholders, including analysts, engineers, and product managers, to understand their data needs, propose solutions, and drive data-driven decision-making by delivering actionable insights.
• Data Infrastructure Monitoring: Continuously monitor, troubleshoot, and enhance data pipelines, leveraging CI/CD tools (Docker, Jenkins, GitHub Actions) and orchestrating workflows using Apache Airflow to maintain operational efficiency.
• Leadership and Mentorship: Provide hands-on mentorship and technical guidance to junior engineers, including code reviews and architecture discussions.
• Documentation and Governance: Establish comprehensive documentation for data architecture, governance, and processes to ensure scalability, compliance, and security.

Qualifications:

• 5+ years of proven experience as a Data Engineer or in a similar role with a deep understanding of data architecture and cloud-based ETL/ELT frameworks.
• Strong experience with AWS (preferably) or Azure , particularly with Glue, EMR, S3, Lambda. Databricks, Snowflake, Synapse experience is a bonus.
• Proficiency in big data technologies such as Apache Spark, Kafka, Hadoop, and Databricks for distributed data processing.
• Proficiency with Python libraries for data processing and ML (e.g., Pandas, NumPy, Polars, Scikit-learn, PyTorch, TensorFlow).
• Hands-on experience in building real-time data processing and AI/ML-driven analytics solutions (SageMaker, Bedrock, NLP, Power BI).
• Ability to architect and manage data lakehouse solutions (Iceberg / Delta Lake / Hudi) or classic warehouse solutions (Redshift, Snowflake).
• Familiarity with compliance and audit requirements (SOX, SOC 1/2, GDPR) and implementing data governance and security frameworks.
• Strong problem-solving skills with a focus on data integrity, scalability, and performance optimization.
• Experience with CI/CD tools (Jenkins, GitHub Actions, Docker) and data orchestration platforms (Apache Airflow).

Preferred Qualifications:

• Experience with advanced data architecture principles (medallion architecture, materialized views, task scheduling).
• Experience using BI tools (e.g., Power BI, Tableau) for real-time analytics and operational reporting

What we offer: 

• A role in shaping the future of protecting the most critical applications that run the world's business and a career that grows as the company grows.
• A unique culture of high achievement and teamwork.
• Supportive and humble colleagues are the space's top problem solvers and innovators.
• Financial security through competitive compensation and incentives.

Location: Onapsis is established a new development center in Bucharest. This is a hybrid role (1-2 days per week from the office), so candidates must be commutable to Bucharest every week.

About Onapsis:

Onapsis protects the business applications that run the global economy. The Onapsis Platform delivers vulnerability management, change assurance, and continuous compliance for business applications from leading vendors such as SAP, Oracle, and others. The Onapsis Platform is powered by the Onapsis Research Labs, the team responsible for the discovery and mitigation of more than 1,000 zero-day vulnerabilities in business applications.

Onapsis is headquartered in Boston, MA, with egional offices in Heidelberg, Germany; Buenos Aires, Argentina; Texas, USA, and now in Bucharest, Romania; and proudly serves hundreds of the world’s leading brands, including close to 30% of the Forbes Global 100, six of the top 10 automotive companies, five of the top 10 chemical companies, four of the top 10 technology companies, and three of the top 10 oil and gas companies. 

For more information, connect with Onapsis on LinkedIn or visit https://www.onapsis.com.

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