AI Engineer

Overview

Design, develop and deploy machine-learning and deep-learning models and solutions, including generative-AI, that address complex business problems, working with data scientists, data engineers, software engineers and client stakeholders.

What you'll do

• Lead end-to-end development of AI/ML models: from data ingestion and preprocessing to model training, evaluation, deployment and monitoring.
• Build generative-AI solutions (RAG, Agentic Workflows, MCP Servers, Conversation AI Agents) aligned with business goals.
• Work closely with data engineering teams to build and maintain data pipelines, feature stores and orchestration frameworks.
• Develop and integrate AI models into production systems (APIs, microservices, cloud deployments).
• Optimize models and solutions for performance, scalability, robustness and cost-efficiency.
• Monitor models and solutions in production (drift, bias, fairness, reliability) and implement remediation.
• Document model design, experiments, data provenance and solution rationale.
• Stay abreast of the latest AI/ML research, frameworks, tools (e.g. LangChain, LangGraph, MCP Clients/Servers, Agents SDKs, LiveKit) and propose innovative ideas.
• Work directly with clients to understand problem statements, translate business requirements into AI solutions, and clearly communicate results.

What you'll need

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or related field.
• 3+ years of hands-on experience in AI/ML model development and deployment.
• Strong programming skills in Python and experience with major ML/AI frameworks (TensorFlow, PyTorch, scikit-learn, LangChain, LangGraph).
• Experience with vector databases (e.g., Pinecone, FAISS), RAG and Agentic workflows.
• Experience building or fine-tuning Large Language Models (LLMs).
• Experience deploying models into production: building REST APIs, microservices and monitoring.
• Strong understanding of algorithms, data structures, statistics, and machine-learning fundamentals (classification, regression, clustering, deep learning, sequence models).
• Ability to work in a fast-paced, dynamic environment and adapt to changing priorities.

Nice to have

• Familiarity with Text-to-Speech and Speech-to-Text models/solutions like Deepgram, ElevenLabs, Cartesia etc.
• Experience in computer vision, time-series modelling (ARIMA, Prophet) or multimodal AI.
• Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker).
• Publications, open-source contributions, or personal projects in the AI/ML domain.

Details

• Location: USA - Remote.