Company Description
Epsilon Innovation Group Inc. is an R&D-focused consulting firm specializing in climate change, energy, environment, blue economy, nature-based solutions, policy, risk analysis, and AI-based big data analytics. The organization collaborates with public and private sector clients on complex, data-driven challenges related to sustainability and resilience. Team members work at the intersection of advanced analytics and real-world environmental and policy issues. The company values innovation, scientific rigor, and practical impact, offering opportunities to contribute to high-impact projects across multiple domains.
Role Description:
The Artificial Intelligence Engineer will design, develop, and deploy AI models and data-driven solutions to support R&D focused AI solutions in energy, defense, climate change, healthcare, agriculture, transportation, water management, urban planning, environmental protection, telecommunications, education, logistics, technology innovation, and beyond.
The AI Engineer should be able to turn models (especially large language models and generative AI) into reliable, scalable, observable applications integrated into products and workflows. The AI Engineer role shall focus on production generative AI systems (RAG, agents, LLM integration) and is expected to bridge machine learning, software engineering, and product needs—handling everything from model integration and RAG pipelines to deployment, monitoring, cost optimization, and evaluation. The engineer will also contribute to technical reports and presentations that explain AI methodologies and insights to non-technical stakeholders.
Key Responsibilities
- Design, develop, and implement AI/ML models and systems (classical ML, deep learning, LLMs, generative AI, computer vision, NLP).
- Build production applications around models, including RAG pipelines, agent architectures, multi-step reasoning workflows, tool calling, and prompt engineering (using frameworks such as LangChain, LlamaIndex, or LangGraph).
- Handle data preprocessing, feature engineering, cleaning, and pipeline construction for structured and unstructured data.
- Deploy, serve, and scale models via APIs, microservices, containers (Docker), orchestration (Kubernetes), and cloud platforms (AWS, GCP, Azure).
- Implement MLOps practices: CI/CD for models, versioning, automated retraining, monitoring for drift/performance/cost, evaluation frameworks, and observability.
- Optimize models and systems for latency, accuracy, cost, scalability, security, fairness, and explainability.
- Collaborate with data scientists, software engineers, product managers, and stakeholders to translate business requirements into technical solutions and integrate AI into existing systems.
- Evaluate new AI technologies and trends; document architectures, lessons learned, and best practices.
- Mentor junior engineers and contribute to production reliability (e.g., handling inference cost spikes or quality drops).
Required Skills and Qualifications
Technical Skills
- Strong proficiency in Python; familiarity with TypeScript, SQL, or others is a plus.
- Experience with ML/AI frameworks: PyTorch, TensorFlow, Scikit-learn, Hugging Face, and LLM APIs (OpenAI, Anthropic, etc.).
- Knowledge of generative AI techniques: RAG, embeddings, vector databases (e.g., Pinecone, Weaviate, pgvector), agents, fine-tuning, and evaluation metrics.
- Software engineering fundamentals: APIs, version control (Git), testing, clean code, system design.
- MLOps and infrastructure: Docker, Kubernetes, CI/CD, cloud services, and monitoring tools.
- Data skills: ETL pipelines, big data tools (Spark, etc.), statistics, linear algebra, probability.
- Understanding of AI ethics, security, privacy, and responsible AI practices.
Soft Skills
- Strong problem-solving, critical thinking, and analytical abilities.
- Excellent communication (especially explaining complex ideas to non-technical stakeholders).
- Collaboration and cross-functional teamwork.
- Adaptability and continuous learning in a fast-evolving field.
Requred Education and Experience
- Bachelor’s degree (or higher) in Computer Science, Engineering, Artificial Intelligence, Machine Learning, Mathematics, or a related field.
- 2 – 4+ years of relevant experience for mid-level roles (software engineering + production ML/AI systems).
- Must have at least two relevant case studies/complted pilot projects
Preferred
- Experience with specific domains (NLP, computer vision, recommendation systems, robotics).
- Background in full-stack or backend engineering before specializing in AI.
- Familiarity with evaluation infrastructure, A/B testing, and cost/latency optimization for LLMs.
- Contributions to open-source projects or a portfolio of end-to-end AI applications.
Role Context and Variations
- This is a remote contract role.