DOIT Software

DOIT Software is looking for an AI/ML Engineer to join the engineering team of a B2B SaaS product company and build machine learning and AI features into its platform.

About the Client

Our client is a SaaS product company working in analyst relations (AR) and B2B influencer relations. Its platform helps technology vendors and B2B companies manage, track, and measure their relationships with industry analysts who influence buyers in the market.

The product combines a specialized CRM with a searchable database of analysts and influencers, analytics and reporting dashboards (share of voice, sentiment, competitive intelligence), and an analyst-facing self-service portal.

About the Role

You will design Python-based models and services, integrate large language models (Anthropic’s Claude), and ship them alongside the client’s software engineering team.

The platform runs on Angular, C# (.NET), SQL Server and MongoDB. You will not be the primary developer on that stack, but you will work in it daily: pulling data from it, exposing your models to it through APIs, and pairing with engineers to get AI features into production.

You will report to the VP of Engineering. This is a full-time, fully remote position that requires at least 3-4 hours of daily overlap with US Eastern Time for standups and pairing.

What You’ll Do

Machine learning and data science

• Design, train, evaluate and deploy ML models for classification, clustering, recommendation, NLP and forecasting use cases.
• Explore, clean and engineer features from product data stored in SQL Server and MongoDB.
• Define metrics, run experiments and report results clearly to engineering and product stakeholders.
• Monitor models in production for drift, accuracy and cost, and retrain as needed.

AI and Claude integration

• Build LLM-powered features using the Claude API: prompt design, tool use, structured outputs and retrieval-augmented generation (RAG).
• Develop evaluation sets and automated tests to measure LLM output quality, accuracy and safety.
• Optimize LLM usage for latency and cost (prompt caching, model selection, batching).
• Use AI coding assistants such as Claude Code to speed up your own development work.

Working with the product engineering team

• Package models and AI logic as Python services and REST APIs that the C# (.NET) backend calls.
• Work with Angular developers to shape how AI features appear and behave in the UI.
• Write efficient SQL Server queries and MongoDB aggregations, and help design schemas for the data that ML features need.
• Take part in code reviews, sprint planning and standups, following the team’s Git, CI/CD and testing practices.
• Document models, data pipelines and APIs so other engineers can support them.

Required Qualifications

• 3–5 years of professional experience in data science, ML engineering or a similar role.
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics or a related field, or equivalent experience.
• Strong Python skills, including pandas, NumPy and scikit-learn.
• Hands-on experience with at least one deep learning framework (PyTorch or TensorFlow).
• Solid understanding of ML fundamentals: supervised and unsupervised learning, feature engineering, model evaluation and avoiding overfitting.
• Practical experience building with LLM APIs, ideally Anthropic Claude: prompt engineering, tool use, RAG and output evaluation.
• Experience building and consuming REST APIs in Python (FastAPI or Flask).
• Working knowledge of SQL (SQL Server preferred) and a NoSQL document database (MongoDB preferred).
• Comfortable with Git, code reviews and Agile/Scrum workflows.
• Clear written and spoken English (Upper-Intermediate or higher) for daily collaboration with a distributed team.
• Proactive written communication: status updates, clear questions and documented decisions.
• Ownership of features from prototype to production, not just notebooks.
• Curiosity about new AI capabilities and the judgment to know when a simpler approach is better.

Nice to Have

• Experience with vector databases or embedding search (e.g., MongoDB Atlas Vector Search, pgvector, Pinecone).
• Familiarity with agent frameworks, the Model Context Protocol (MCP) or the Claude Agent SDK.
• Reading-level familiarity with C# / .NET and Angular / TypeScript, enough to trace how your services are called.
• MLOps experience: Docker, CI/CD pipelines, model versioning (MLflow or similar) and cloud deployment (Azure or AWS).
• Prior work on a multi-tenant B2B SaaS product.
• NLP work on unstructured business text such as documents, emails or research content.
• Understanding of data privacy and security practices for customer data used in AI features.

Recruitment Process

1. Pre-screen call with a Recruitment Manager.
2. Technical interview with DOIT Software.
3. Online call with the client.

DOIT Software is looking for an AI/ML Engineer to join the engineering team of a B2B SaaS product company and build machine learning and AI features into its platform.

About the Client

Our client is a SaaS product company working in analyst relations (AR) and B2B influencer relations. Its platform helps technology vendors and B2B companies manage, track, and measure their relationships with industry analysts who influence buyers in the market.

The product combines a specialized CRM with a searchable database of analysts and influencers, analytics and reporting dashboards (share of voice, sentiment, competitive intelligence), and an analyst-facing self-service portal.

About the Role

You will design Python-based models and services, integrate large language models (Anthropic’s Claude), and ship them alongside the client’s software engineering team.

The platform runs on Angular, C# (.NET), SQL Server and MongoDB. You will not be the primary developer on that stack, but you will work in it daily: pulling data from it, exposing your models to it through APIs, and pairing with engineers to get AI features into production.

You will report to the VP of Engineering. This is a full-time, fully remote position that requires at least 3-4 hours of daily overlap with US Eastern Time for standups and pairing.

What You’ll Do

Machine learning and data science

• Design, train, evaluate and deploy ML models for classification, clustering, recommendation, NLP and forecasting use cases.
• Explore, clean and engineer features from product data stored in SQL Server and MongoDB.
• Define metrics, run experiments and report results clearly to engineering and product stakeholders.
• Monitor models in production for drift, accuracy and cost, and retrain as needed.

AI and Claude integration

• Build LLM-powered features using the Claude API: prompt design, tool use, structured outputs and retrieval-augmented generation (RAG).
• Develop evaluation sets and automated tests to measure LLM output quality, accuracy and safety.
• Optimize LLM usage for latency and cost (prompt caching, model selection, batching).
• Use AI coding assistants such as Claude Code to speed up your own development work.

Working with the product engineering team

• Package models and AI logic as Python services and REST APIs that the C# (.NET) backend calls.
• Work with Angular developers to shape how AI features appear and behave in the UI.
• Write efficient SQL Server queries and MongoDB aggregations, and help design schemas for the data that ML features need.
• Take part in code reviews, sprint planning and standups, following the team’s Git, CI/CD and testing practices.
• Document models, data pipelines and APIs so other engineers can support them.

Required Qualifications

• 3–5 years of professional experience in data science, ML engineering or a similar role.
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics or a related field, or equivalent experience.
• Strong Python skills, including pandas, NumPy and scikit-learn.
• Hands-on experience with at least one deep learning framework (PyTorch or TensorFlow).
• Solid understanding of ML fundamentals: supervised and unsupervised learning, feature engineering, model evaluation and avoiding overfitting.
• Practical experience building with LLM APIs, ideally Anthropic Claude: prompt engineering, tool use, RAG and output evaluation.
• Experience building and consuming REST APIs in Python (FastAPI or Flask).
• Working knowledge of SQL (SQL Server preferred) and a NoSQL document database (MongoDB preferred).
• Comfortable with Git, code reviews and Agile/Scrum workflows.
• Clear written and spoken English (Upper-Intermediate or higher) for daily collaboration with a distributed team.
• Proactive written communication: status updates, clear questions and documented decisions.
• Ownership of features from prototype to production, not just notebooks.
• Curiosity about new AI capabilities and the judgment to know when a simpler approach is better.

Nice to Have

• Experience with vector databases or embedding search (e.g., MongoDB Atlas Vector Search, pgvector, Pinecone).
• Familiarity with agent frameworks, the Model Context Protocol (MCP) or the Claude Agent SDK.
• Reading-level familiarity with C# / .NET and Angular / TypeScript, enough to trace how your services are called.
• MLOps experience: Docker, CI/CD pipelines, model versioning (MLflow or similar) and cloud deployment (Azure or AWS).
• Prior work on a multi-tenant B2B SaaS product.
• NLP work on unstructured business text such as documents, emails or research content.
• Understanding of data privacy and security practices for customer data used in AI features.

Recruitment Process

1. Pre-screen call with a Recruitment Manager.
2. Technical interview with DOIT Software.
3. Online call with the client.

We are looking for a skilled IT recruiter to join our team at DOIT Software.

Role Mission

Turn technical hiring requests into strong candidate pipelines and successful hires using an AI-enabled recruitment process.

You’ll own the recruitment flow from vacancy clarification and search setup to candidate evaluation, interviews, client process, and hire.

Your job is not just to run the process, but to understand where it is weak, improve sourcing and communication quality, and automate repetitive work wherever possible.

Key Responsibilities

• Understand technical vacancies, clarify missing requirements, and turn them into clear must-haves and search criteria.
• Launch and manage sourcing across LinkedIn, job boards, partners, and AI-powered sourcing workflows.
• Monitor candidate quality and continuously adjust searches, filters, and sourcing logic when results are weak.
• Manage AI-assisted outreach and qualification, reviewing and improving messages and communication where needed.
• Review candidates, conduct pre-screen interviews in English, prepare structured assessments, and move strong candidates through the hiring process.
• Own candidate communication through client interviews, offer, negotiation, and hiring decision.
• Track the recruitment funnel and use the data to identify bottlenecks and improve results.
• Improve AI Skills, prompts, workflows, and recruitment automations independently or together with the Automation team.

Must-have

1. 2+ years of hands-on technical recruitment experience, including hiring software developers or other technical specialists.
2. Strong sourcing skills — LinkedIn, Boolean/X-Ray Search, talent mapping, and ability to build and adjust search logic.
3. Strong candidate evaluation skills — CV screening, pre-screen interviews, and ability to assess candidates against clear must-haves.
4. Fluent English, including conducting pre-screen interviews independently.
5. Practical use of AI in recruitment for sourcing, research, outreach, candidate analysis, interview preparation, or similar workflows.
6. Strong ownership and ability to manage several vacancies independently, prioritizing work based on hiring needs and pipeline quality.

Nice-to-have

• Experience working with AI-powered or automated recruitment / sourcing systems.
• Ability to create, configure, or improve AI Skills, prompts, agents, or recruitment workflows.
• Experience with LinkedIn outreach automation tools such as Grinfi or similar platforms.
• Experience using recruitment funnel data to identify sourcing, outreach, or conversion bottlenecks.
• Experience improving recruitment processes or reducing manual work through automation.