Job Overview
Launch or elevate your career with AI Engineer Jobs in Boston, one of the world’s leading centers for artificial intelligence, machine learning, healthcare technology, robotics, and enterprise software innovation. Organizations are actively hiring AI Engineers to design, develop, deploy, and optimize intelligent applications that solve complex business challenges using cutting-edge AI technologies.
This opportunity is ideal for entry-level, mid-level, and senior AI professionals who are passionate about building scalable AI solutions, machine learning models, Generative AI applications, Large Language Model (LLM) integrations, computer vision systems, and intelligent automation platforms.
As an AI Engineer, you will collaborate with data scientists, software engineers, cloud architects, DevOps teams, and business stakeholders to develop production-ready AI systems that deliver measurable business value. Candidates with expertise in Python, Machine Learning, Deep Learning, NLP, LLMs, MLOps, Cloud AI Services, and AI frameworks are highly sought after.
Job Title
AI Engineer
Location: Boston, Massachusetts
Experience: Entry Level to Senior Level
Employment Type: Full-Time
Roles & Responsibilities
- Design, develop, and deploy AI-powered applications for enterprise and cloud-native environments.
- Build, train, evaluate, and optimize machine learning and deep learning models for production use.
- Develop Generative AI applications using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures.
- Fine-tune foundation models and optimize AI pipelines for performance, scalability, and cost efficiency.
- Design and implement NLP solutions for chatbots, document intelligence, summarization, search, and conversational AI.
- Build computer vision applications for image classification, object detection, OCR, and video analytics.
- Develop scalable RESTful APIs and AI microservices for integrating machine learning models into enterprise applications.
- Build intelligent automation solutions using AI, predictive analytics, and recommendation systems.
- Design feature engineering pipelines, data preprocessing workflows, and model evaluation frameworks.
- Implement MLOps best practices for model deployment, monitoring, versioning, and continuous retraining.
- Deploy AI applications using AWS, Microsoft Azure, or Google Cloud AI services.
- Optimize GPU utilization, inference performance, and model latency for real-time applications.
- Collaborate with Data Scientists, Software Engineers, Product Managers, and DevOps teams throughout the AI lifecycle.
- Implement responsible AI practices, explainability, bias detection, model governance, and security controls.
- Develop reusable AI components, SDKs, libraries, and automation frameworks.
- Perform model testing, debugging, validation, and continuous performance optimization.
- Integrate AI solutions with enterprise applications, APIs, databases, and third-party platforms.
- Participate in Agile development, sprint planning, architecture discussions, and code reviews.
- Prepare technical documentation, AI model documentation, deployment guides, and architecture diagrams.
- Stay updated with the latest advancements in Artificial Intelligence, Generative AI, LLMs, Agentic AI, and cloud-native AI technologies.
Technical Skill Set
| Category | Skills |
|---|---|
| Programming Languages | Python, Java, C++, SQL, JavaScript |
| AI Frameworks | TensorFlow, PyTorch, Keras, Scikit-learn |
| Generative AI | OpenAI API, LangChain, LlamaIndex, Hugging Face Transformers |
| Large Language Models | GPT, Llama, Claude, Mistral, Gemini |
| Machine Learning | Supervised Learning, Unsupervised Learning, Reinforcement Learning |
| Deep Learning | CNN, RNN, LSTM, Transformers, Attention Mechanisms |
| NLP | Natural Language Processing, Text Classification, Named Entity Recognition, Sentiment Analysis |
| Computer Vision | OpenCV, YOLO, OCR, Object Detection, Image Segmentation |
| Data Processing | Pandas, NumPy, Spark, Feature Engineering |
| MLOps | MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML |
| Cloud Platforms | AWS, Microsoft Azure, Google Cloud Platform |
| Vector Databases | Pinecone, Weaviate, ChromaDB, FAISS |
| Databases | PostgreSQL, MySQL, MongoDB, Redis |
| APIs | REST APIs, GraphQL, FastAPI, Flask |
| Containers | Docker, Kubernetes |
| DevOps | Jenkins, GitHub Actions, GitLab CI/CD |
| Version Control | Git, GitHub, GitLab, Bitbucket |
| Monitoring | Prometheus, Grafana, ELK Stack |
| Security | IAM, OAuth 2.0, JWT, AI Security |
| Collaboration | Jira, Confluence, Slack, Microsoft Teams |
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, or a related field.
- Strong foundation in machine learning, deep learning, statistics, and data structures.
- Hands-on experience developing AI and machine learning applications using Python.
- Knowledge of TensorFlow, PyTorch, Scikit-learn, or equivalent AI frameworks.
- Experience developing REST APIs and integrating AI models into production applications.
- Understanding of cloud platforms including AWS, Microsoft Azure, or Google Cloud Platform.
- Familiarity with Generative AI, LLMs, prompt engineering, and Retrieval-Augmented Generation (RAG).
- Strong knowledge of SQL, NoSQL databases, and data preprocessing techniques.
- Experience with Git, Docker, Kubernetes, and CI/CD workflows.
- Strong analytical thinking, debugging, and problem-solving skills.
- Excellent communication and collaboration abilities.
Preferred Qualifications
- Experience building enterprise AI or Generative AI applications.
- AWS Certified Machine Learning – Specialty.
- Microsoft Azure AI Engineer Associate.
- Google Professional Machine Learning Engineer Certification.
- Experience fine-tuning Large Language Models.
- Knowledge of Agentic AI frameworks and AI orchestration tools.
- Experience with vector databases and semantic search.
- Hands-on experience with MLOps platforms and automated model deployment.
- Experience developing scalable AI APIs using FastAPI or Flask.
- Exposure to responsible AI, AI governance, and explainable AI frameworks.
- Contributions to open-source AI or machine learning projects are an added advantage.
Desired Soft Skills
- Problem Solving
- Analytical Thinking
- Critical Thinking
- Innovation
- Communication Skills
- Team Collaboration
- Leadership
- Creativity
- Continuous Learning
- Adaptability
- Time Management
- Decision Making
- Business Acumen
- Technical Documentation
- Presentation Skills
- Stakeholder Management
- Ownership
- Attention to Detail
- Research Mindset
- Result-Oriented Approach
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